spike: What cost, latency and judgment quality does Jev trade against a general-purpose OpenAI model on identical real review inputs?

[usirin]

Question

What cost, latency and judgment quality does Jev trade against a general-purpose OpenAI model on identical real review inputs?

Shape

logic

Run

9f11845c

Came from

standalone

[usirin]

Jev savings map, 2026-09-19

23 spikes are closed. One matched-model comparison remains open because both OpenAI requests returned credit_balance_exhausted. No production integration was made. This is an experiment inventory, not a claim that every possible use has been exhausted.

The strongest result is narrow interpretation of existing review text. The strongest measured token reductions are batching questions that share context and trying a short input before fetching the full text. Neither is a measured reduction in the whole Fabrika bill.

What the experiments actually showed

Work	Result	What it supports
Optional document selection	44/46 essential documents retained on 15 held-out tasks; about 60% fewer selected bytes against a broad baseline	Missed required context. Keep integration on hold for the planned baseline PR.
Duplicate ranking, four candidates	7/7 known positive targets ranked first, versus 5/7 title overlap; 0 false suggestions on eight negative queries at the tested cutoff	Promising candidate ranking. True targets were inserted by hand and Jev saw bodies while the baseline saw titles.
Duplicate ranking, 37 candidates	6/7 targets first versus 5/7 native	A larger pool exposed a miss. This reused the seven positives.
Live duplicate retrieval	Native title-query retrieval found only 3/7 targets; Jev cannot rank the missing four	Retrieval is the limiting step. Current bodies and no historical cutoff weaken the replay.
Jev-selected search terms	4/7 found versus 3/7 with equally budgeted title queries; union found 5/7 with twice as many queries	Suggest additional queries; do not replace the original search.
Shared-context batching	86.7% fewer billed input tokens; median serial 1329 ms, parallel 281 ms, batch 201 ms; 72 compared choices matched	Batch independent checks when the same state serves them. Raw streams were lost in disposal; a correction is posted on the issue.
Controlled CI diagnosis	13/20 labels matched	Synthetic cases did not establish reliable retry decisions.
Real CI diagnosis	Jev 9/13, native rules 9/13	No overall win. Two native unknowns improved, other uncertain failures became unjustified code diagnoses. Gold causes were not validated by repairs.
Evidence support	22/24 controlled cases	Advisory warnings, not proof that a task finished.
Controlled intake	24/24	A small synthetic result, superseded as deployment evidence by the harder real-data tests.
Real issue types	26/36 historical labels matched; six disagreements survived confidence 0.90	Confidence is not enough to reproduce triage. Labels may encode decisions absent from report text.
Review scope	11/16; only 3/7 required findings recognized	Do not suppress findings. Inputs omitted full criteria and diffs; historical judgments were discretionary.
Repeated votes	Three calls per case kept the same 3/8 accuracy on a hard subset	More votes did not fix these misses.
Review intent pilot	12/12, versus 10/12 for a FAIL-header rule	Extract whether a reviewer requests local work. This does not override the verdict.
Fresh review intent	41/41 on 12 different PRs; cutoff 0.90 accepted 35 with no observed errors	Best narrow result. Six still escalate; small correlated sample, not a calibrated production guarantee.
Short input then full comment	42,845 input tokens versus 77,237, down 44.5%; all 41 choices matched after 11 full-comment fallbacks	Lower model cost on the same dataset. Median combined latency 147 ms versus earlier full-input 128 ms, so no time gain shown. Six still fail the acceptance cutoff.
Fact versus human choice	29/36; all seven misses sent human decisions toward fact research	Do not use this to remove human decisions. Title and question alone lacked planning context.
Supplied governance conflicts	9/12 exact; five conflicts caught, three consistent changes abstained	Advisory check only. Retrieving the right rules remains separate.
Precise source-backed claims	20/20 hand-selected claims	Useful when the claim and decisive source are already supplied.
Automatic comment scan	Initial whole-comment and sentence scans missed both known stale claims	The precise-claim win did not become an automatic lint win.
Hard batch versus single calls	Six matched choices agreed, but both modes caught only one of two stale claims	Agreement does not mean correctness. A repeated original batch also changed an earlier answer.
Malicious instruction detection	24/24 controlled examples versus 19/24 keywords	Possible advisory detection. No security-boundary or production-cost claim.
Read-only tool reuse	47 identical repeated outputs, 247,823 bytes, 2.98% of a narrow read sample; none repeated within the same file	Not evidence for a large exact-cache payoff. Most compound shell reads and other worktree directories were excluded.
Matched model comparison	Jev completed the smoke case; Luna and Astra returned quota errors	Still unanswered. API error latency is not model latency.

Where this fits across Fabrika

This groups related skills by the kind of work, rather than treating every skill name as a separate model capability.

Skills	Candidate use and boundary
report, triage, front-door	Duplicate suggestions and intake hints. Retrieve enough candidates first. Real issue-type classification was too inconsistent for automatic writes.
review, build	Extract local repair intent from old prose. Future reviewers could emit a structured field and avoid the model call. Keep review obligations intact.
heal-ci	Add diagnosis hints where rules return unknown. The real sample did not justify replacing native rules or authorizing retries.
governance, adr	Compare supplied rules and claims. Retrieval, authority and final judgment remain separate.
grilling, wayfinding, graduate	Detect missing evidence or explicit questions. The measured fact/choice classifier is unsafe for deciding who owns an answer.
plan-epic, check-epic-plan	Semantic coverage hints may reuse evidence checks. IDs, cycles, dependencies and ledger validity belong to deterministic checks. No planning replacement was demonstrated.
diataxis, writing-for-agents, write-pattern, glossary, deslop-comments	Optional text classification, relevance and source-consistency hints. Precise checks and automatic scans behaved differently. Full mode/style quality remains unmeasured. Document/rubric integration stays on hold.
operate, campaign, ship	Structured state, permissions, retry budgets and merge rules belong to code. A cheap model adds no value to facts the verbs already provide.
architecture-audit, prototyping, handoff	Candidate evidence selection and supplied-claim checks may help. No replacement for source investigation, execution or preservation of evidence was shown.
build-ui, review-ui, taste-color	Jev is text-only. It can inspect supplied text or token names, but it cannot replace looking at the rendered page.

The vendor's use-case map is a useful list of possible decision shapes. These experiments covered ranking, retrieval, classification, verification, guardrail hints and routing inputs. Feature extraction for prediction would need outcome-labeled data and a separate evaluation. A task sounding similar is not evidence that the same threshold transfers.

Cost accounting

Jev pricing is $0.042 per million input tokens, with free outputs. The short-input review experiment cost about $0.00179949 in Jev input, versus $0.003243954 for the earlier full-input calls. It saved about $0.001444464 across 41 examples. The larger opportunity would be avoiding unnecessary expensive work, which has not yet been measured.

Do not compare only uncached input prices. Luna lists $0.20 input, $0.02 cached input and $1.20 output per million tokens. Its cached-input rate is below Jev's input rate. Astra lists $10 input and $50 output at ordinary context sizes. Actual bill savings depend on cache use, generated reasoning, retries and what work is genuinely removed.

For a replacement policy, measure:

net savings = avoided existing work - Jev calls - fallback work - repair cost caused by mistakes

A serial Jev request also adds a network step. A cheap precheck that never avoids downstream work increases cost and latency. A prediction that saves a few tokens but creates another repair round can lose badly.

What I recommend next

1. Evaluate review-intent extraction without changing decisions, against an explicit repair-needed field. It is the strongest real-data result, but structured output may be the simpler winner. Confidence in this next experiment: 85/100; not confidence in unattended deployment. Follow-up: Review FAIL text does not always request a local repair #9494.
2. Keep duplicate ranking as a suggestion and improve candidate recall first. It can reduce reading work, but cannot recover absent candidates. Confidence: 75/100. Do not auto-close reports.
3. Batch shared-context checks and try bounded short inputs with fallback wherever a helper already earns its place. Savings are measured inside Jev, while per-task quality and two-step latency still need checking. Confidence: 85/100.
4. Keep full review, governance authority, automatic retries and human-choice ownership on their existing paths. The observed misses are too consequential for these replacements. Confidence: 95/100.

Remaining work

• Complete the 28-case matched OpenAI comparison once a funded key is available. The pending request to the user already explains the quota error.
• Measure actual downstream reads, model usage, repair rounds and wall time in an observation-only deployment before estimating monthly savings.
• Compare against structured fields and deterministic parsing, not only a large model.
• Resume document/rubric integration only after creosb's baseline PR, as requested in the conversation.

No monthly savings estimate is defensible yet. No threshold here is a general model-quality guarantee.

Evidence and cleanup

All 23 completed spike issues are closed. Their disposable workspaces were removed with the repository tree checks passing. The baseline workspace remains available to resume. Existing local changes were preserved.

Early spikes 9470, 9471, 9472, 9473 and 9475 lost raw streams because capture retained run metadata rather than stream contents. Each has a correction. Later experiments explicitly archived exact inputs and results and verified the posted bytes before disposal. The retention defect is tracked at #9482. The real CI false-rate-limit example is recorded at #9474 (comment).

[usirin]

Blocked comparison checkpoint, part 1/2. Exact public inputs, request data and quota outcomes. No credential values. Concatenate base64 and gunzip JSON. Decoded SHA256 0a3eddc63698532623e865d156d282d9b839b8eba36facb53a388b6f624c74f1.

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zrzfmff/Gub9Mx/A0VpuT0JeH3bREAoqEVzZ9Mrb8ZNmTZ7ppFUpYzElj1wNTkWX0u74G6XxTG641diXstgf3oJ7CaMgwcRRMf2Ip7OdHb+z43d2/M6O39nxOzt+Z8fv7PidHb+z43d2/N9jxz+WMX+6s+N3dvzOjv/XCdOHODwYnAkS8gdhWkuOnbIUMy1gp1eaDS1WnDIjKBCifb9qXPagnJA8q8m+WnH6nQ2+s8F3NvjOBt/Z4DsbfGeD72zwnQ2+s8F3Nvj/Ahv8bGeD72zwnQ3+r2GDK2/VEtJ6QzN0mlPTZoFQ/NpCT5mq6bLL7K50qpbQX06GbsxchOs3KNNk0nwr8Z4kQYlsI3LKLPo37Gz3ne2+s913tvvOdt/Z7jvbfWe772z3ne2+s93/V+TBn+9s953tvrPd/7m2+/H5YNN275+dXJydXQ4+Gz8Xnk/wuHImDIJUIdqV6EHUUK/ePXv9+ud33//+5ZsrY3sgYpGcc1TE95Kr3796/bq3mDKeTjYTWqThVr7MNOp5HulUnjzHuZCM0AKMd3vrdsC42zybdyeFPJndlKLbqTVXGrQayJSAPmYyEuojQ4a9KSU6v9rZcaLQZ8IBZlCSgI2yqtY3t0mjAGopvRldky8pUX1wTInqJXdimSApSrOZKwLjZUSCqQv5531XpAWIXN4t9CoTXjhJJ9RpB6zQRzAO5bpf8ZZFjtNV1CI8V9tgh3DrJ2DodJWOCIeiNi8R57xWTWHjAPTlSwWs5SvcbShBsqrXwvNaq2lKgwzFY2NR/uHxJ5RxoiEo9tEI40vk/4Rt7Ssin63j77Jl8utf/xpcJD8wHQeirij3r/daK369d/CU0wxPUnXCk4S/UvdDYFxAqIEubuiGJAvR4WClRJQtxBDdcF0CZw10cyjKxOGDlwW14ndF8+16bEfZ0K/EDJzVevx+VX59xMVKI60KVtVMTjIAa9rvrXVb00zOYgmTa9ssDdFNVvRYYfwwldS2ZC5fuhEiIkDXdalGMSGVPg2naKSRPkSy8qUsqv/CmpzM11PibKXGfoQ2nE1gqgV2t3tDvUxRcu7zlZL5jWy1wWDhY/hIxxArFa9HMZNI9inRdUB5tP8XYg0/OIDuXBLOC+dRuJPBGqUTmgxdRRTysz4iuF7tJ+0YFfao38/OB0f656meZ+US9pmG8jKby8uvfnz7zbPnL1+4ndVxqbmQBnshGytYovlBzZ7AEtyJgXRjoIoxJXLq3zuQP/ldiPOry37/cpSkgcMtVwpTlCq6UWtVOKThWTJMnyap8K7lKqGQwUvcjuZYIE81o+T592++ef3q+btXb36XJkdGUFcQD1fEwBwlL169ffeH9KkrQoKcA6JgTQ7tDV9ggjlkvRJaLnyi946L/XR7DwN0kTXA7MLFl+a3iY8ilVMYN4ANTMS0EoI2LDScPGramSlqns3mtegFpjMYF7oWTaRcAyZqk2pmDqIRxjYxpKaqDdE2caNVtDUqgQRcw+Vn0yltxpgDQsoIgbR4pepuQPSiiLg1gEeeW0qg1A+mUGg94NaJFjJvuOuwuQyHTvgJrUyqR2DJkeg1u9FcGh/EfByvxeB0Qg7fbyrglcpiCV9zq+hP1pNaj4xYntV76GEp5XIxu/copfAvdLu8LMxlna2mgc17Ia+qAY+dvo+yvHauD1HyZSJU350zhnepcgMXh4Fcyg7U65sb1UL1HnN+pPAEqbrrcLiEVgwcVd4uuo3zrpht6bmHsz6r2YxOBzLq1AxAE7krxA50Y+xncqn3+dxw8YQp3jY6pJ7QcRWzfBsVwhzATINTCGrO3a36W+Ihmwyf1I7rqdxpZPchlh9dL8wA/CSmPKybcIbjoVnixITLzOdKUNuunKa3L6/eHdEt8v+8dui2cvZ7/vD/TC0wVZgzNqpdViQ4oB4m6Y9vfv/m+5/epKaoZY6l4Nin9sIPBq9HzNAf3kK0mZ+ixVcK0UGFhTWyqD0GHtQk5vlR7Vg0VfU1QIMDcKRiQHJeYhbL6sjJhQsMLjp+21IzAxPl+tOSt2iLUTTOuEJ2uvP9FgYbyIU6/lpMNag++LFr6xp5bRUO1fMprCzMZ9lqMUsoXmn35SU9gTg1LSYhmpU8KSQkRkUBOhSChTaEUyb2nLukwvoTYML4yFF6ED/iPaVsizTPZw117h6ncXFx0fdnrqR7rkt/m2yTGyM5zE0F8jqkED4MSKLUhRmEofxetdmShtYwj3bUppHTcoOzVjRPEz7l1EY+VmiMhseHC6IAg25fAt4hLnwR3iEFD40At9sT7awcoSDq4JjeK0/WDNmQY8GJSVAueDF14zX4NgPqM4Nv2Kb6CYQAAbflPvhJxBZ9j6Dld1sgA+WIZbI6o4eggWknENQt3DdCldynv9fDcrHzsOw8LDsPy7+Oh0VV26KE3kZuZc4Oi1/L5iZL4WbOB4EEASL8V4Q31jjTHcIp9NTE8qar8No1n7GW8+Vyjac2nSxlDgRlNetcACE85SyOeJRQ69dLdNab7jwyO4/MziOz88jsPDI7j8zOI7PzyOw8MjuPzM4js/PI/B/mkXmsXuVy55HZeWR2Hpn/P3hkzo9PLs8/65FpSQhKs7sMDoj1krKv+CiXnWsFGSnKlv0m0ob22o1T3EhoIp2Eq9zl0xtkpL9jK6mGzffU0PcNoHSNQG/OGZM35LbM8Nw5XHYOl53DZedw2Tlcdg6XncNl53DZOVx2Dpedw2XncNk5XOhwGfR3Hpedx2XncfknI4QcHz9ACOlfnp2f9z+NEPJc5L3sxE9CWBVbH2s6CzcJJlDcwBqvBqmORf/I5+qpEI4q+4EbV8l9biWjdoPIKXTmBAPUl8WlzMu1EPAkefbDK1AT8QgWFXTt7e6VZ74KkxAkILx7dvy8ofCp7kSMGFRGyjtglFVzdAee1tY+OOBRaE28jV+hEzrXZa3mFY71Mir7VG4/XWV3qlipatNUywQVUXO2NzbFutRT5ZV4G569CX4cFDjbBIomdqvQV/EJl0pXVNDlsj5q1h+y+VFP/+B/e1YgDSXTrXNFc4OW55WWa7PwPsUg07+C6cgoQJwi0WWEf0tVBiVYj/SOxGB4A+mysDf8oOW7aWfjzdZ/fFJN84/hkpiDSJxyNd0iYOg8o5SurUw2pcRIe5uzg4qhnziKquyDnpvbhtOi5z7itenvuf+/XRXoDA/bfp/vP0hFlWmN+DWeSw/0u48/BXVqY1RZ0eWWHoG46qPWs3QRoKO4mY7yEgyL5C4m8JVIYHcrtGyxnteNGl22ED1beEeud8WKb9h/sDZ6EOujMV93FL9aRmETM5QJ2/gtk/Gv0a8rfotpzI/RCB1TiUcu8GNNaTjoBOhFW6uGnIsNo4a4DorEz/F+ekhiSXDz8RGObAZ3SYTaQrbEoXxPGdPDv98ILxSp+P1K97BnLzhIO4kdbntYGJ17rpSzsi+7be15i4Vq5pH1M7LdwNy63C/sjPkcTfMPWBgOrRhXvjo/G5wnb4e9IZL3Mnll2QQsDCwZHGQLMXDwDoiZCO1CGJxQCOVkaIHOT3ciHACwattpsifPrXW8ZMjXpakIRR1sM885TUCZDgOFQh91a7bKbZLUXInh5L7Ir/WSZ+W96K/FR2cvwZ8RhuDcVkqsHb9cbncjr0zjRKec9Runzcu/brKlVtIDKWU5z+AhpUgzNJcmL6eRLmLeSWFBDZ+AlFZ1jwAQI+OnD06vqDDjeTV5P0qu9zLDulLPBm0V0/EnKjJbsABerw+bBOMV9GkaJhbyrpAh7AeiOLje+1LeF4amyojX3WVpOjLciG8nCzKhTAWgSUPy3eTwUBTDRgx2J5Eefv8xyQLfkQJ+EGxLPvnOcTkbUlnFhPPw89Z5SxRdGUMAoImgIzYW2vBGQPyKD/7V+cXlSe96T/+CrTGXE7bgLlMTMLJJdU/2CU4DB2CSqp7zSqjlI93MwPg48MyBZodiOFEf54wRVKDJRfloxpbDdaCzbKFwOJAdvQd7SfSGI+oQDu/qt/iHLKoMwekaqjF8dXE+PDakClq0VIt0TUTLoCUgQ1MehrgGzHRjJ/hBDEpPKOr8kYlFPn9Tl7rcGSVvVWtmYtBcl0a8qu/wpDzmg0z25ebuWK2Rg2gx4FVWNU7NaxH5VMlt9MRUSTse+Ke8dzxCbXijHBxt0Q3tfXMYj/B2G3obUFPuPZciPvy7wGyMBa0U4eqBcE8JW+MgbCLiuy6JRy9viNwtyiNVzXgAoOK+uMpJbkD1gBsEO6nW+XW5nz5//ezHFy9/fv7ts3c///TqzYvvf/r57ctvsARw9YHHCzsYnhxEnqimHWTjSWdQIiXZp/EHS+iUfuVd4zznGIXPwNFDjSDAB2BmwZFVJTJOhlRzZWa8HbbdhluKERChy9Pz1IOWgO3U93KgRNIwchBO3BKJ7MHRE8CWniO0cav4SC4wqszDgnbXpQLWqPPPNjKzexRSp81lwmcUJQ1kSWYdRgPOsZ9hx7teBa/z+axbTB06jyz87/Nlo/FRb2LqKbdwQoZdzWpA771cClnKagxOXahJ45ZQ1TVwKtr3dWn2okU1g38IWD4OFsczr44JEPUJqfORniFPgF0shy4E1heHBNuuojJy90Bl+7u9N4Od92bnvdl5b/7J3pvh5UPvzcnp4Pz48vM9VjSwLqYCEPBEMsrgZTfXfM8dQcIiFWuigKnCEXnepx5GS4wxlV14Xg6diKcmQPQ5QC1qW3Yfb6lDGE4WcQmFG+JRSB6eIY5qkSF5pt7u1yHKTV3ni/E8n3Y/1F0dihAL4hVNHgV1VRhPNF2GQ1lZ8khGydKlIz8hmmJGPL9Gg2b6SnWNINqQrd6vl6KpPbcJ2Z+rtJ2fMM3r4qakEvcMn34uX36FGfUwLNXfphVFUoNYExcl3bj15WpVrdJYWug4vKJgUkJRE532C3mWXlloluZKmqbywevy6Cj5kgHWH514bP/y22oqptqv/EX7xtdQbcPVb6rV4mvNkoku0tw9enD9nSiTYzmh+suvjjbf0n4114P3RneGUcg0Odv2/Wkci4pmrdIRFHHE+56JcqEbcxA8GegAbGFDefG6ztsrsn/Qy/Ub8AkErwVVUeK7gaBqR/TeDNYolEs0EgoSiq0RcFkSVMnzNga79YkM+tEKPlLoZWT78t5iarY9YoutcyYajcwht1rDjA8rRbdpKBxBBsYNv08Ia7yqsqk7+RoC3owlwjmFOOySyRBlHa1plwORFeniwzWTU+4yAoQynWQfRrKNSYEqba1lNlw4qPy1rQ3gmuXsZ9qM2ZwmE4RsRcfcO9BMm+lKZKw7jT/goyFsHiCkFe00/5hROlKy0qPs+MTSGjxDsVw5yy9TLNCnohyJ+jaghT6lv0SeSl+V9VJ2A1RAX+bLj01ewkJ4UYjcuEkNZZRYtrr1wj5eerJRqQxdy5w1n46gP/DvEmD0ITdSuC5TlrczFssx2rZw+vRyTTWDXit1W1dzkeD5ZE2/POaVu8l2jTszrdKMsUazpa5LM3CfQv09H4p2or4Z3QxzZW+dMcbmVBeeNJdoIUJBrMoaclszmqBWTADl2PjsS5m4uo1kOuvxCOHM6fTVIrvJH2fWEVd4qxCeHw2RFRl7ubKU/QbkO5Wv9ni/MoFegVfX3yMRZu/gIFXzBrYuf6CsNbf4w+0ySnWiQMnitpiKkqJCOuXOpQ75HP7MgnjrKVLoXmLRe6qYpQ4ZNXw6/aZAXp2MH9MwluVOGlkUTMjcSXoqlAqsS7xxXUTiCSt7GOsyG/IlBa1Ssmw1MF2hPuQrEZc+I+TvsSqGO6tiZ1XsrIp/dteIh1bFyenJhTDwT1oVPzl8/FmeUQGRyUI97yCwm7sWDursqJMfS3jESh8ddstvGXRcEpepTGYFLUAToeqlep/QIGKSW3INcJ+70wJ+jUmu1MCGEs6m0Ywa2W4h7HwaDykyRbhP3Tq/UX/YYlzcrFlPAM9kW6X4gvYTgy/pPUFoiP2o+0PHCN+dMBczo/sXubP5JJ8yGMW0LbiJffiLEWr63X206CZzoPow8By6dlF6zy8zYE1kczBKuUSCNrHAwKlTpVy2PIjc3SIKEU2arLynnvvpHhVANRYJcZcpNnP93utgrq/nA5Bjl20FFZiZzaJmwHf1XX3jkPVtmUZGZ2hNAGB9rWDA9D+xXB1EYF3EzF4Ebx/YxAJqRodH9ka90T7GOfLw6xDt6l8cUbVJXTgvcwTe1ewBZnhrdCptRy1TpeuRWXvG1tJW/gKzm2vRE/gXkGNKvkF1KxfzXBQhtXcZaHaU7aJjhJNGNphROwCi4TN0Szj1cdmZnBocE+SFqn+byqaun148AhPSIJNSVZym8T6/J7h2qvf+Pr9PXdAj9BZ5gczMo/THH+Q/37x6e/VO/nz9jH88//b7769eyl9evLr67tXVFe94/e7l2zT232LALHVJokqXVBQ6OI8NN5/qKEUzs+Qai7U7gywTKuRehdxs5B2nP7hxPyNCvBbLMITgzuVtcXPLpEg9XkLQbquKacxSsMgLIpfLPtG2VLuntPgm7Smh41auDAjWFXX0Et2Y+b0TAOnHdOQ2Q1Y6EPLobJgmiK/UzNksPuJWLkqLOKOCh7vb+7gLQ1l1b6pa+Z2slZw0/bqretAwtsuvFIGmyiD8NWacGeVEn3B88xb50H+WnXwv/7uR//0uVZHv7B7h1gvZsrnT9DEmsAdtcHK918pkLVYLS5JkkCkcSo8uL8Oor/dM79aLP7mb4BpwzEDOnu0PVvDkYtA97Z+nB/plo5MRgyJCA3Km0jff//z8uxdXXo/uaBarIwz6ZPxwEKtRp4JCKUPtALS+qjxszeFjvKhMKA2+Z8scizqky14hAkgC1jWzpM9xzmMOWxYS2Ef1oXZMg+ywBX+rb/NeChdKyd25d1sXmSaLh4zADQq8wPUmoOdNlgv8d+J73MgY/sfV929kPpA8RRnFw3xXh6ZqZDppUesRtDFa9r7pt6l98jvtAJFaSxQtINEzYu4Q6FXaXIoJxj6eosj5bj2jTiM3W9tQWGrA9v4o9uyfK9evZV06I9lSGopV1KyCbgCWE7U/xYZQj3SxoJ+jdYatSRSkuBjCQr+aYmI69HrlOZBu0pPadZhyusDaCiqmT1W/otMDJ055wN9tux3vbLed7baz3f41bLe4Y4uw9qXGc0oEkHX+IsUYaNaGTwtNCWBYviawnboN28m/Jh5YzyEGE7SsibbGK7SXnzNB3Jc/lcm7s6x2ltXOstpZVjvLamdZ7SyrnWX1v79l9RhY+OBkZ1ntLKudZfXPrpQ8e2BZDU7O+xeXp19gWXFokwLaZ+OAvPNaju1Y5o0aiW6huSTWqVTY4tigV5xclrUXGrRFw3u6E3a+zJqVaCNRjzakxyPK5iEl8EJXf0gsD25vrXk0wjm3G1xiY3wnCl3y/JW/UR/coHC8kagvaD9pN3atYWzJUg2ldU1OFllfOXiYVX6z0oz6epR8dXF8NhReqlxDfkD6A8gkYxl/12EhGZ1jTio6rJHtVxeDi1P/vOE+BIWKq6j7QMyixUITm2gZ5FMTkbJMk0m+JH7HeIzbrXn67wHjQmZundv1VUQ0CMujR7aVOOkPFQA9akofxyRsSEi1elg6xkwMn+rTST4DqhDAjJIUGzgTHvF2LVbNtKhFcTDMM03xJwlog9Tm/mmSwkyVe+tnzbc0e4Qb3fERSMCrb5/RrpF1WLiKrGfKcVGEa8I/teWYFB55RZVDDo5aSh1+EZJcA5qArbKFAkR0UjTf2cCZrl579ZJvhrHd9QRLfYh5pNizgvXCFXJYkXxS5xPdtGckfNYWaKY7j0fZAjdSXZotwpnqpqcQCa5pTxf1yA1LOMKqa+TXu1/MYZ7N5QCviAnCU6sdyufzn+14/Nxk6Ap9FNUb6WIkDliJMGkyUtoAqGo2kBOrUJtwn3TL+n3hnieTy7Pjs9nwfDo8np6c9oeXp6fnQhKX08Fwlp/kef/c5zBCKRXllylcmZY1uNPj5tQxxoHzWo0rmUoQYR7KygjTzjQUMEBN4LyFKgo5hKHoUUv/3nxvXZGZc8c5/CzsK2E+n6KvmZHj+kc/oEUFbFJuHeiSGnB0PD1dyvJZQ3K25S4rXThWXKpi6B/LwXFhXJkYY2sEz5lkSJyfcmFM8GaN1sVTnVE3JG6Gkhu+gbhYPUe1wvFZWy0qccHm2WQudTyKQHHGlLQw0HqYp1ivX8NeyrEYsD/9TD3XIcXYEliBpcx5zV0lm/OpFZC0GEctVzS7NBNjG+2KVRUwERRWlFT5w1suoxWvmegN0Ild1lECierG+V9g5zsK6y4rmf69Q0tyikA2Fc2lXQYzFhaJ8pn842S+Zp0V+lOv61t/An8WbrZEWvho2+mcFFtOZVyeuvkWZQ0zVbooy97msvA1kYeUS/oVQS7JSl1xrZOpEpFKsNM8XEOPkdx+B50pzxYtPJ2jbInHZMrgarXlgCYAB1rCHCybh/KgdkAyIo45NL/9hFawjF3PQVk9JNriV2enF0MVvWJ8mN9jFXHMWr6NTtm1P8oejmm1nhM0D9uFWqjWZ3sQ2CcXoeQIrxR6uNETqT4YeYMQPQEBlfNgYd3qP7VzqsxJPvU+hkRYCM0TMsoUbuWu2KlcedQqV380dlasQhkVPMgTaqTfVguI0O0INqIhD0J38TIcemoOXdU8VL+HF0eY4C15Oj5aigbyFHwFFC5Dwvu6E9EXs+KGkxaF6e8270535t3OvNuZd/9s866/pZQKBaxfEjgzJ6Cq6Mnbl89efPfSjDHNNCyXC5gaEyQwrhIPvBAOk3MnyjZUUL2swMreKBwHQRJ51EcEMi2QAhUEj6gTIJP74CZX5cnV+BaGk6dWpdor0FCoMJETW2akjFBz/V2tktaJbjUVv9cq0mnwUd5VpBcmaVZqMb1DbTsDJb3ImmHFe/cueI/FKBTiZolTYkgGiHzUKDd58fKbZz++fvfzqxdadnO95x+83mtV+2y8loZS8GM39Whw3IcjGomnNENoBxogYzoKKwoh8wvV91/MlBI9rIpsSjHiITDSaAqRZ76JgnJafnyzquR0q2NTPuX95q808mSILuFlpIz030G1aw8QYFazgbEpoogCkbgSdcORsQ1xjm/AnFA818mHAmHA3JXYZwa6yPM6enyDHgN6YPo/E3vjlTC8Dm4ikBKhcRzKoh96vOEQ8UuipegBi2L/es8cq9d7B6lVvAGTpH6v0+sQihiqA86D1kj9uFhrE7QbrdMBlmy3f9kdnD9NRBAx/jI1Jk7gFL+RDrfg8DCcaVLZAxKVzRtcDuMfUEiBkKj5q4XbPNEQME8F+P+dw09xR5poG4bhR1897oxusBfQoi3hR98HYnHivgagSUAq/dtP37/9/bu3L1/iHz/Zj9/Qlw76NjJcByzuK125jOYuq8DrrcBRb2RVG1A2/E7vjZHXhqKgdZuER36wPD5Cd6+Ob19wldfeoS+kqqnc43tFYlbg5+4csaj2+673YkdIaiAsukluLUVtzeczWf/94YmNU5ExHidjGaYIYbX+B2dJIAXO6mCE8kPlzqBZt+NbXsetl7XHGwJptM5r674Apt7z7yfOyee+ERcu9f4spicAklXUoMbuqbECsUZSWXPU4tRHh73Nc/qU2EePHGJ5YVPbP90XPhQsW41v2hAtttRgsYrJhZSB9iSA39GlUhWdkvZkhE1+eBHEl/D2h2toBAEUmcA3DoX1dz41K0VySZU/JQGhypCp+KNqF/BNIYTTGHs6MB9UWGU/PxHoXeBj9O4zsTkPDxGpZJ4ANaixTAC30KIBaBdP3xuD4Mma6Bjx3JsfUzQ/Yb2RlOt4QHJQYWCWAJ5y9OIIRK51UOhmTt+fogMZHozA2Py1by2Pp3Xxf8py4EJR+xcRUJvf8JeerW5qx3h44bklXrQuvpxBv25f+mAg8f7KN4UsU+vlCrTVfpWNYePKun3TW1qw7UvVnc4o/oCi+riZbs5Bh5jsa+1bG9cwblWwTdWAKf+RmGpGQeDSVz88e/4yRnMhDsunX6ThYTG+V/nMQdVDXUGYNnUs2BIs5F3b2c6RITwpESrgLwXoyKcVuUMcUIhS6yKqygtEUSzYJwD5XnrAMuLJErGvEaVXvvPcB6brCNZwiWNWrZHHQdkArXJR3PDMKP/Sk/YEbTvk8tRKQk2Tjgcg5/S6pG4UQQ2Y0PaFzJZxEliow9dP4K7UjAHkXwB1WCP0kLCZxxaLPDC1lzsPS1UVLWwuimtTLPDOOi8RHxcB09JQI0BoN1aDNburXGaG84Y9ISoVpaJZ/2gw4jG8bFseqotHSUul1LyuBRbIwcnb4LD+miZAfCqqFBvY02bXjGTWYGZP2ho3GW9scQh/byrRMSGHI8WsKBX57zGJcNQSA2LgzteukW6kHR6mW5SjtgQhaprqu3bs/LijYYtdshjnq+s9GWarFPjBuDzXHw1O7TQ4Rww+Nc7haJMPwv/nMeOQ1/MooXi4t3VDpVt1IhhLbhTJsjB/GpYeRPGmCr7ONPLbG6Bis16NK7d0qPfuuXY0tdyOIvVQo59qPbFS0QO97amKrHBFRRvJcC5Wb7RUh6khbjnLN2hKhiWfEyXKQaUKOy1mrBCfFp9Ynlg9tpwnEP1SE2MgTukCeEQvgFLpcEWRWFYnJ/0Tww2VkxgMAStQZ84P+UV1pwBRQssyDXOnlGh2UmS1H5HBLanNPK9urFUOyuBX7KqReFi3jnYYgmvlRksi/Qqo2bpcFR+A1i6qDN1oJC9hn+89WuW4YBlkssrGY2FKTP1z6gK1xT0bfKaQuPVtcECQ0/Dx+vHFHpyeE0xKk6Hyqa3xx0QVPng3020HJOI1sjlhVakxm/bd4dvxGlg4BumK/KtPbv3Y8P2q2RNrR4QBUhPryhhFCcUQv+C8pmKAfchrZTsgm+4q6XYt2vSkd7TxisMniXVNQKcWPlUisg75Oy6QuixPG1Rar3fUe/CCaAyPqaBqxcfSFd95ZwAYmgeMCbt0Xb6SPReCjw0MKOVIEEQWUvv1FEDC1XJBRcWvWARlho8wyOHbQ4Ft0nFuTs/9ry5PBucHMbyGmaogDi95XK7gF+wgR13fMmkOekkyltVihLWrQedJyEPU7M6Cn/Y6eEeRcZJY3mtzCy7dKGBA6gPOw/Br92/tIBNp94Ohu2cfouH4RN098g8LI5NNrOL0wfF9JKURoMWaJlG0IneNrlwOgwW/iBjkAnoiaf9zzd17dN0QhaAFvPgA22if+AseWIkepia78e0fFHWSD4AHCVnTbEgVuTCvnUD3UTVQlEpTpGmqGf/4aADpj835TN+Ckc9sNXMGURm9u/Y8XM7+V5fH5+TB+9d737nUEeoG7K1FhJh78wzaDlmHgnDOhV0eRLh3Tz3mHT/v4098idI7H+zK2jEv1CHnOWoosXcBNK92yZKyHq1xyCoswMwaKLcgDjAyBc/tQnpvQiybOxG4wF5RK7z4MAYf3qhB1BSJqUip9UEk5ghoukDDOJPCa5fddNlPLcQUJJjHKU/vsntEN2RvcN6IPhPnoYxzdrnTXBnR+qnuPhKq0oBuHKvCpEKcSn/vfqZhA5+Ru99UlgbgmtX44Mn43olhnFv1LOMW9av7H9wTmj2jUUD+Myq1YOOMjbjT6/irFqRmdqbesS3C9Jah9NanYUoiviH7+e7bV1c+l4s9gxzyjXINJrG4EakbKjJOEDu0p3/jRzVjtpalSSAzNQpCOXAVIZG10N59WLlO1MEwK5nl7JoYzevKUml7SorFzA+JsTK+0OU4Wcj3m2evXmsCunPgt99aWYuhriLXw4fKkBLGq22JoiQHH7Ki2RTiR8R2i8KA+dSHjRjUFbbHcRhisGbs0IjbRj4WJpI1XbFzo6V2uY1m6yIDfkXEemXqNpTEaTFp/pGRo7MHiYEnp8fD0+HgLI4c7ekIu9NqMtKJ/ntymk/HJ/0sOz/Lzi7H5+Pj2fTiYjCc9ceDs8vjwSwfnmfTLDvVHntlMzrOj8eDfjacZOPzWPDKS62ah3wyz8qnTqlAaHS5VKDrLEh0l6aemdO5mit0XkSw42p6P3Kv1x5a9hGFz0OPSo2nqjgnAGv7ey6fjpgcQZvYR+3ONLd3yj53oh/PDlqZXzI0bdU0W9NsTdIfnl1dmUaFw0NeSwOgKFmQtC6tlZQfh60Uky/TwUXaieP//i64/rTCwx/Xyf1knlt42XRY+4DCayGeeWWs2CjLH6EUm5yGAiw+Bp97QdbVsZqOlav3oPPhz2vtMuo68EEBjyy6/vHlZbfXo46rSFd6KuTWpxGT4afQBGuVd7F8OsCwULaEQdthyzZNivfbYsnsKosoKd7yvYOOjRJhA0fBoYvWJq3Y2uMwKwwWs7fkoEwVY8WtflM9nGnWhXwDIgsMo66OlvjRdVde08VsuyyY6I7vu7dV9d58tPTe/JCvui16h87zR0z9T6bbTeKYJUjBD8uCgo2W+Fl5IsquDJXSVWGY1+u2WIq2Mwm5nZo749Tc8N4IhT0nerCB4cFg1n4HHSYkoOxALn0rJg0vIL/lJrpHyEZ2j+ldPgvCQ/QX+AUj8RllkAq6eq5sx2HvYQ7U7FYLVfxT0T1LRRivE2cIgCeP/LAQOpl1LTbXccl7aPhQjcf5ypqSdWdZLf/RpD1WUaEYjEPhutYea/o2YzDc0V8QeB5RzFeRTItVE7lPqlqxjZksACbDiBozCkLQSlNEW0tiNT2qtvrqn8ApmFOFxFZwOo8KmvoJ/oBvRgZGt+s/2aXg/zU7ddT0TdMfaX1ySRY+GF854ObLk9NLHraC0r2I48ssUAr0CteXB46211kOCOuyqNvTk0gBTmXb6WVUY6Mjrs1YWS4z1axHY7pMLfcH6dv7myIvc3eE3rKZoOnVIPFnUQtk9hjUzEJ/EZ4yJALYtPx1mc5ybXm+sGcyFRobR7Eq49NaNPaNjRYAztnrFDbVtqPDGnUV8l2eefKph/YHF5edpD/s6973j4dnZiQ9W5A/Kbte5ahLElG86a5NXvhJzSlYzNVbh+Rp4qg/3WB//7muLPPYBG9YQMX4s24Cwo/aPYlAl/apyPLjEoDVju9908Wop6gle95r6NO4BSsS12ZvjL3nLV9lyvexCEjweFGgtWw+aJBXh9I47v1L5I6UloarpaLIJ3J9ZV3P4cATO+GjMe901ZKBtjNVI8NSEs6Ltv90vfL+uIBqb2UVq2JmNaAFSyyLjCNOR66tJIbeUS3JpqcxJDlvpTWm/GYlA9dGrxrSgKGmSnO6uYU+P0ZT4TJ36pXfdoJRiFapd5YOlkUbHVF3Nq6r1dh57a7WS2Sz1y5lwynZnxIrRhPRMcH3PZl5cE7wXWb3IjkTy1c7dXlsGcWZpvFEWancNTe455htzeyA0ANVuHsxK7gPVWlBhkWeyQ4aG/d5TjMFmESCvus0UXz0Amuci/YjZ47hbCRzMMxlfW87DriZja4jIWMEtZ5MNO/uzgHSRfYVDOhJE/N5n6kg270qxq4LulKp+XtmrE2MeHWb99R5AD2QD6wLzRhmTzNXNcgcPlmyTpDTURZ5K46Wt2el2Q0uYcSgqwNQtcpMLRywvfmhWjVRm9VUCzyX4WpXr7AWQQ4Gz0InGZwcnzkH7rB/Go6Wi+FlAeZgkusGeurWNhEGuud+RUr8KlPAPTP24Udzw1xBfk9WmRzY/RScFem0Mm1tloWOVDL6H+YsgKlvZfh0y/85W91A1Mgmq6C5ERVVuMP13jOvqmnnBy1XhfVZh1ar1gW4MBABB887QTT/ei/JCVXMfilzpiUV8fFy1QLgIa4UCM7AoLFrw1DTzbuOCTfsFxeycF642gXGFZ2pM/VXZVPwalkFtENWaX3egTW1Zg6nOzN5HucvqNj2lkjAfuAgwAzdB4CUCM1e5jJKaA1wZP9Tn+yalBsF//7lu+FgNBiM+uf/795n3UXmxtl0GA12DqOdw2jnMHrEYXQ2PD3d5jDCmf7He4xCAWkoLdUS0mCbrfKGOe2R/fvFboDIylA/wP7pI5l5m/WMMOOP0gPHKZ+/wgdckWkorEq63busaAjXkTcN8h2R/MDyDQ9lcnKqMs5Kak/6qh0AdeA0qd+ziZbIugsoxPKjryWKeolrPlUd1Wm5MlqhKKEHvr8DIaDVlesSFb5kN5pIzXqWGSWSMyKguXyJl8I5vXjSiHvQqnB8umkOR6Y/3AuxFlFr8HS+FlWqW9+XE60VVWO/ZDsqJM1smLj7SD2r2dPSV+ZkCPOpwqANPDeMYj/CGsr3qrm369TGUjdAvdhLniuWv4u3wX7xKwjniJn1Slic7lYPh4p6wi5Yczq/iP91f4CrLK03Fxxr+KN/F4QzWd9qpUk9Kx/uT3/zmyQ1WcDsg9SMgm53ibbcDO91/5Js9xvYwobFe1Vqrtcy+EzsfXPCsdchJVNbb6qSfw9fI636eB/mxXttoaYMG+4TH1ve6sVQbudU1HmVTbtj4c+qkoeuYb6DXFUqgNI3oJ1Uk7fEvBu5ZpeEykAuerZUjy6AjVhl41Y2pJPAKyj/OvLAOncurrxkcjFbQyBOxlaXszZxcLPpHnpAZ0ifMIfyf5U8REdkRmyLyKLU68kmWIvm96CxRd7UT1tr47uTLp4a11zlxocdmNJGv0iUz73nWeaejtxhZNXBDSVgm3bIFdunDoxtnmnjOZ+ZJKNDZgGw1I7g2YdEYGJcw29mYKzeolr0tkyDDjNv4EW+L3OgMxePYfQugrO5wqrIKYCX0yV5/8oI5GvhDZuspbN5JpT/qA87XFyhS/TKJ6jPRJ+gvvbU51+ZI8o6DiIWzOCIW2ltgRDi5LbHhuWycFVLIkAbalfYju+FvISgCRxENuU8ij7zQpvBJfvuvNi/wXvNPG9cjQZNBpDMQYeh32rz5TFnZyu4RkdHdasePXCz5ZrJE3F/DN81JAqOTIUYI47ZxgNavd96ypUorWqXgQ9Qnjq0lDSAi4RsAV7NyXw99T09tL8KU7U6DK+4NJVMuZiv6gOteK9nXMzdKoW73tt0iMtBRQyDNqjz27UM92ku7DM4fWHoyf1qzrskPPhQqAJhs7z3JGABFb4djXbV02J6Xuh9NuIQSri8Kwe09LsH7ljhrS71/I/QBP8kZOQDJz7w1o6ifbGaB1dipOK1s6y2amd4Yc0qqv6p5ainV3948xxZzT9evXwR+s52TK+43lN7G5qQAiupGPDxjPJh2MNjwm1ubEfrUDUkIuI+eTZruKe+AtU8QebawoeZZjd6GDTZqjyZVvHEYgs0G3wVi+UNM5g47EelefQSgDKZBzoLuS7WedglSDfOnR+iSndwGqyXU0MECJ6SWGV//vqVnamlpiiaeTIOojfwFhJay8tVWKTVo8fZIMx76hvXMokRiBwOO+Webgabb4fnCRYnzLHvy+BAdArtb/3ZmKiL8BVr3kCrpt5GeHPG2SwDChuKlKYZRUPquEpQg6/3DgOeWNtJxs7Fd1WXmcxUyuGHpjiifDGXngJyBI+42iSFc+2bzzLebjoyH8otfSnflurWkzsEddH17iIKqHdFUoDpNbcFDKcEX6BlGtOZbM6+OnFhB64feZ9XIylhg+9WhN6C6ZzCPLK7q2Yqy/D9jOoBBDDTixTIY6S6j6WX+egSUy0hh9YLa2MdlEpRY+19aCMsXzLNqL6F013TzMjICwdjiHcZNQJCtmTRKfhQvuolv6sqDL0hAou61WltPTXvHNuVA/NC68vdisJdUXs0C12Tty71nyq3zP6VNZjv/iVV3yHcYQvzvGrFJGAOVhr9Q+5wkATpf/wh+VVTfZ28+fF18ivQ0Ncb6JgtNURVNQpgYcl1dMR0b12ywVbLZcLuihD6pl7lBSRUT44NqTj3rUmWoT8hD6s2jtVaspHGHXQjS0WMgs+SuZfp2yTJyCIxH/UwOSPj7XdpAEkCgej2ce1Lepg8jF3LgYmyAvprUJ/NEauYl6k5I/bZZtha5/dAmYHQpeKQaaGC1UlPFEzFNdF13b9UJWS8utZQgXeY0VJs0OR5tbAEdT8CzxE0M4QBcS+Gg9KlMQL45eaMNV7vubtiyeG9d58Jtif7mop64KLudLQrHYfQtR4hHF9RR/BRM3p7FmEUIqXSWkcJG+3KFycsvBUYCT5mmRCqIag5f0aRh1XnOAextizn0wsx+6z3sOwyt5VgIj8RCyOz7NeybfVxXaNK8jByimuPWlnfZep1gSvT++g0aYeoieZ8QY5idu/q7doHTnWKqOIHvTlDV7L/cNwLjFoDIFrrikIxYVxkB13HDhit4qbNicKcvjXD4nnqXvgH98J9K3gQDWhZqKjW1AsROgaz+R+KY2NJTKjElklxphNX4WuarGq8fvOFd5tHKhb6dAKzkzlDrMZu6VHTghPPTlW5MrwBBb2SAQA/J2o0ODXO4r9qkDCWxlstxoUFc+X/sWrD3kkf+VqYpspWx18yl9ot4ilNROdj4U76m994W50Mg6AblrYc4SVbVyZ0cyJni8ckRGGjaoXZNF45Xt+INY8egKGp4/hezzddEmBVul313xVtiXdBI39WOLPhROtssZNim0o3PmgcelJBb47eHVx16zEcPe1jCw1lI6GBwXsN+Y3aj8eOBwOYo5rZhZoZ+EK5TaNUdDgFkyLSt9muYezBr9ncRgl9veSFhpMsRFCvx6gsR2azol4xaPUPiT8NLr4g/rQ1XXm4iz7tok+76NN2oJvLs9PTi89Gny5OL2enw+xkPBz0s5Ozi+n5+WU/Hw5OZ8OT4eS03z89PZlmx1MffTqfDMbTk3x23D89jlyNUXAI1PO0nYLsNlWVgsGTOiSJtgIl/8OHn1I3sNRryF9dngzPRFDwIynj4sJNzo2bfkH2swK/I8OJVk005OBksZ7N3pbMyi6NTg7cnI228yH5Fgm5PjvYmQY21y0zTQYDwi1iPsH2teqtR+rzWPQdyuF8pG7DoxSbNI8nQh+CExxqKrST7D4LO04QNeUkL1vt350LzkCcfCZkrPjFmVGhCjxkSPsqddbcbo2c+To5/PhL8lXyS/Lc//ZL8pYoo8kv8lu32/X/w60D+fkTRY43dJqoacvwgGUNdj5ThBUy2X6hgPO2JZ+qVZfqqq8r9GZPnRK6Cf5h3v8tVbsoSxoe+zJVZeRz7imL6qitwV5Ij9y/moo7VxeLwgj/vM/hHQ/6/f+rt21mCOo4ECWMyIBcdbrp4OLsZHZ8ea7lhMMDxUiKcDPUw7Jxd+pjZunwYnZ6cdwfm+o9yE+Hk+NJP+1xy5JhtEcGF6LFr1onsAVRZXPVt8GJYB4AD8Gv/kmFF3nkUMndNqJjjGg7HpV/rcPtKOoIpaPX6x2gnNHwpsA6RA7lyD68j6EKNsb/2Cnf2JPR4Pg0jQeW/DppjeqpbqMHqLCWPdEIHQwI/r4BF4JLm2ghuNYGC8EVj9MRw5EEStYuNB9D2Azl/SLeDKhKLRsuUaKjg8qZIlAjRKwo/Uepw+NCS5TcBdkD3hR6hcBlNfKQSVuwvAx1xepzo/1UmdTgwHN/uImysv/212L6twSZsOPz5N/+aqkHPdAg4F3+luKu/fTBtvTPT6xAUH8jIofrMW4FI/Fi+FRao7cT0NtWFBEYtJrUUEXIcp+BAhEy6Lm/GwlE3oXC9cXYIMJtiCSBgBwSVQviRKP+ci9O1SfHxLPFOPQGLhALb2vLWIUzdl+RM7Yf/APdudSGlYrNtGw1ZbAFPZXJaauIfOntqtDvXm1rgDtMKdMNNWOTlxO1alUWEQbfIyc1eRwSyQM2i4TnDJ28CUHtxUOoj6M2dtxncOKyT2N3aKyUWvInIDNwtlA01fwXYDPexDHXwhdrG4IHlzXK8ObWnMky/9aDASQeWO6vbi3/lnbacFEdh7n4aa752UL90fAy3fo9BWWG3+vslK3tY5YuLGHEjAKWn8PXp26/v0EOwmBpUfRoeOKkxWMi6SHAlb0nWtthGqWlquPWCo25hueUmG2UqhiiqrNVm1AXyMYq/iVfVf6EQB3booTQScJ1U4/6Y/JqRMWDCzk47ve9MGhhP+gg8BagAo96vU0oNJvjhQzUA00E9iW8LSfiVLepbyrRYUDckyZN+olYVsjlsu5CyZyH2oBPmMKVqot2k46KZj9VR2gNKGTLE2mI3i7cfjbuHw8nF47DW76aQuw4yINRMjhPjpJj/GeI/5yfiWw+PJQJHB72LM0t6NKYveWykYX6HLV2Z4eQsYagmyLXwnPGyPm6TPZbGXCj7RlwB+3EwHyiwJ9Oo/ZqOTjwtMhuSrFggBg9NyQhmfJviwosmbGo2GkHh4SQeOPZ7iX2bBiK/bnxPjrm8GSI00QqYWrE1DmsfnjrdsbKYPR2P9JoHpGVpo7uONJLmbIfQYsYAW6BDNwKMNJB3UOpjrNobKiYPbC8f/mry+twuBV4VfDuPg5K4TFQkdMY3Ih+aaKGaFijTmhl92lEDNWlfg01aFSU+/Yj+zMedKgXxZd54aBjmlL8i1060GD2rw0H+9+hrVvqQmV1E2GVnipt3zC/LrRAKKsAPYXlqYPZ/AssuWDDDfouIugxwgyzZLtx2wmIXvQyqa2+3SP8S+S//CWBX4FVNbG1iJu8fvj2++/fpWDgQRzXEKgOks9+wMzqR4Fpt6vvJwNixkUZJj6jwbmhfcFRXgJsCCv6C8JtWmk9nlvNkYekttCqPhzFkg8PIxP5NMmmDF+Z7u0RzDxGoQHFxqAWlqLjkjlC2p6SquMbiKUFrVyDUkGzoZaOfgdEE32wphrsNZQRN9rKu1LttLEznNBesa41oCt2t3eWOFgmVtFppig7KMqhbjT5VTie4Y5E4Dlt/M+oXChQINNVDanOckRNKrPUii3HbAFIDB2LhOJvZtiUCnsNSaJmDpPuXAKC7xvHEiXdWIIbkRnf4QO0EQL9QndX9/vE0OWo1MrleTbO58aGTV16iLKnuRkRUB+yqCKGrl0hEI+ac1rFdIP4QFb/Y+uJfBo7tfouA8nXIXIMhJ9jqea9ayZhaXzID7EfGy0IqgwwDL3eUKzIXje3yMhgQgeFSvgiegNRXef6avUfS5WYFwUnq5aqNne5RcOxwr5yVkijXs4LZ45p6dI4v68cqgx3fZV3hZQM/s5X1h3IEoUFYoXQ0xA7XiIJjY2SXGTM/IwOrF5Vzw+KVNPKCFMe+WM59bvvAyxOKjH3i/HCkeEVvLP0V4UD0mw3eFT3xNC1ToRJiM5ogpHVcGUtnMUpjSwge5pdZtxCX1yvkU3j0lrXPpjh2jM2UN0cO/5GPaf41xXTOlyhVYduAQSpihtkPCHGcJMtRz6VU06gxcmZiqTNLBzPzCayTbV3+1KpavHwp87QIoHfrsv3hsujahvB2pkNqkSIlFTNyRCLK/Q49B0IdeZem1EzNZiTqpXCXaEnXTPxXGYixGFlwbmH3pbWISxK6kaujBAewLpoctcyqSVU2ome+sk4xREUyOpGVSzSW/9FSz7y0IaM7zIlhyBI8DsUH2OViy1+mVhsvRrzcoY0pSkbODLAgSi3LeFnAn7n74b9Uf9ELLL/fsHZ8S7ktwv57UJ+X9jbYhtC0T8k4hdUJp92fFdpQl9tbdhogGtqluECC9Xn/8UoH2SicBAj7M95zmNr7ouMPp/6xgRCymbXd92agCkRaCIioPKgaZo7LTjT1OUF8cy6kFXGZrVaJfQlY/XYJFBcNZ24FuNbxSEzvXzf9s+9bXRyfN49Ob6MQNmv914pZHkEGoEM2CbyFl7vEYX/6w13I71ah4eGbWoRqyaCJ0WdznIBb8e72IjAPLx+4/JW8PrQ66B23UMqS7PVAgfvvqTmpKCJwdtf5r7NxtcPvG3bnGyxmk+C9N70gmUwMdif92fCH6PmtRHDytLuNPHLeg0a0E97hPGK9jbAjrE+m5TjAJatbQnzgd677GWO9OEo9TXJpi8XqBtOwY7bSqgu5fOEgnJctzT/h75k2fVSiyNZJ8S6t3nWWGvLLyPGs8vuyXk/IkYrbefUetN8ul7mqfZDU/qT1z5oTzDafGKU/PF6T1Uvumb3/n2aL+bF+6Mmz673/pSyekcb2Lz3voiqJAXCQhSdT+yzeXUzSoHO7NFjbhySNzXd0EDFnI9pIIpnrSVOdFiRp+MnbKVZdf59IF/3qkRz9ze3kgoZU6p1gBhe6NL3w9vQHz6LU82xOXT2+yRtwxa2CjDPk3yWXt17nDVtbsBocJwy4O80/cO4nuEQFEovoRDNQ+7xmShF3Hdn29a0fBNUX8WqiWhpoxeS9vm2Y6Zs2ekbJq7m7HO+AZsCfuUq4rjcP92ammge4i24m9SwM7HXR5bJEQA/sJTTLv5iHZNayDGRAavhqI0BU3j6Pp8oQGgC9lCMYPsuwhr3MmfqmjGyA7usDqsU+d5ZC24/LOK93X14aOTSvt864WyUmWiDHc1Z4e77MkFV4vJyGrWNmVbaitURpeYmBjeOpf9Wrt3YrWwI9FoVXDGMeC+E+NBltLWW1LxQ2YLtdXk4hblL5lWlgMfef4Nd4m4/ixDrdC+zup2uE3A3WsnNzKn5Ryev9B/JXtlQG+J6r9jpg7qBie/V4G3R0LIBU2/1tvIw0a5xgzkbP9FygR4ieO1dEj9uud77kePg8XDU4P1OoFnAmNvm70eRr+Hlcfe4f5YeBP+aGqRmWMfzGd+30gE2/OiQmk21Cqy4o3VqWjqxXmqNYlwrhEjBRkJBmri6IZlGju6GrCHUloMhG+FOYWHuXCUAIlu1019fbVQkUO61nc2xh3P0JU7FerJiYc0W5+KmUzEUZrb33fl11VFq+e2x31H9vOaGCphzRFhuYc61PVFPAvLcQ989U/U0q4nvgd9eewr/Xb77dxuE/9/1dgZWscW1KAd/XEzrLbzUe9EfOkuf/9Odoo3jibEr9N2mx1OxAhdLWWp4LR1pZI1HlgtCweu8bW+q0pl5TsM5YtyNcA3V+0gvua1W5Ze4V0dJ272qTscHFphmI95mH3IPNrC5/BvO8uCOxQ/I3sL+vOVHm1C1w6+zqhjDjjqshM9QQc/NkhVywfQiPcR7doGZ1fNtADI6VjyO/HqbX/fp9iT61MW39Cx+BrQxaNgbLSsVmq7dZGVdu0hPfH6AqMAqQqvQaaEDhvdH9aXygjwCC1wUH+Em2pJ05KUBo+s4mzUnXrJzUeMC3CQuZ2Rmfk1V+FL/3QKj17YtNkM3pq04V1HUk8FXxxLsoyWz2PmcKhbKGbeg48Xf1PoXH5lkcGvEcxdj51nhrLpGIv036sqpbpI7rZFztUYhW0g7e9hRVEdmtqn+hj6gDIfLm5r8ITJbGD2Z+11R545lfFkvDhI21FttDOGboDoffShIFb1LVXIguW3JPvKZI6dOFNBy2qgiI/YacYpUurf1ZWqV1UNN2Ls/vLoaBuDcD2o+xma89Qgtc/3JmZEBvwB7ssVSCWYJc/a0B5xDrsbGrmV5X2pZpQjbbbJGPfl66juwPSdxGyLm/7oQUMhiowHJhfgi7/vx6OTiv+99P9l533fe9533/eTk4oH3/fjk/Hxw9vmCm8EwH18Mx/3ZILu8PD6/nJ1Ozobjwez0+PJieDLrX2Sz4+w4O/fu99nxZf/4cnzaP70cx9DrpBXWveLcv1TIAafpLfImDlNu0To0DGjKeq1YI8+B64BYeeQ1JbvTfUBBx8gnJ9JGEs6TzfVLij/aBB9mZEAr6DZqOqtw/NpOFWEMxYIGm+LwO0Qm75//s7MDsvq9ZqLoWOpSFLTbqqkt7UshAPa3BdCPD9Rz+SqC+BUzYe7wO/yIg2IbJmE2cpDByCLxyje/ytoitLD56uL4eAhji0F0LbNcLWLuBawrNAmuiw1B5fK+7HPU52+z+QzFzTcVTfl9gkJ5bcUShFR6OBwka052EMQSZiTG6cnZk1pb9URzg9WcGWo1HduaUrPlO2qIbvsUHN6/ZRCDKCCloapEtnYMtBobDe+t7STWZlEgnzM0hMoQRvB+TZuq7O0SlrD+yzdiY3sDggF3PFWwS5XCaPVc3ZfiKZsJFxaB3sWeXyOm+21i2ytxOSbqAa0NJ+QD8i7UhqM6GNkrY5eC4qmxl/xeNCO7nd1iHSqydqw0W0S9kQ7YO4dNmDuciwnrzliKRlCjyMTVhFvVOkdxAIH5adP1ymCDj6K2nsyWHg5TopqZpw5WMqREZng8Ud87ZQCEMPe9AxUgC6TK2mQ0uXZpHb5IBMTTZeG3bTKzrj42DpnNw7bEp9tjHMiZbly9WV1MARZnbdjNo05aXjW+UyHhW0R2BkAHD09P7w51XFc/ePXyfx6HLLMWMJ7TsSGrHUFkTYuqCUzwxUt90r9IOScozzpbXVLdXI7/eo+A39a7wnE6RfhvQ/+E1cd8cBhfNQEhOf5NyAETu7K3IZdxY2SD83564EsY0nINzEBYKGNY6HqknIEitJG6V30nfKpC50Ei1Lmrb/XbruGLBzquGNaBPp8Cj8Z1/aKzzWjFe/rKgkjjsl2dgBJDKrAcXef8ibZUdhg6vF8hM0nMPGlHwWxze8k3xcf4Al0d2b16b25ZTmkJUIY0o3BGrkdusGA0bhARlSL8KLwRDkcQn1bKQjUvaqW9uScnp9gpLwWQU5rqcd1n45GHB5RH0247l9uolDnLkZE8K8DSe47PXF/tOkP/smy1ONgo9nBQB5ZsRMjJmYLyb5C9He2jH/RP0QrjrCUUT52mTltjFWHLZ0+3E5mBrXkri89W0BHJFapoDDbCdfPDd55+6izqOzZSqQxd3KG+cEUz9zkH4vPuNmxbO7HOvCuGArip/tRfqv9wXtVUqK7wCVYO107hhCjGVP+jJKbS1kt8GKMVvbjEyaVQi85CFGpuoi5PqqS2ITpK9SGoN9gZMXVQQeHo3baaI79PSczsO25nLeFXDfrIaIq2E0yGpObT4fP66FfF9GurI/PwfMqYnirCkdK0h76BNlIYYKW+LHNDgBOJtblMPuklP7h0OO/bcGzAeAfdNM2DswlO/vko0KCH6LYuSppMV9XSBYfHCjYT+DsQPGQrs9JptZGWw4RChA4+wSuGA1Ys6ou/n6Wd5LffXHlXsmFjcSaj4eCUCxl/UnejqzpqbTWOcifKAZ651giKpUs2GPpkltaDe7SVJkanFxcx1I9pEBWyMMNggS2HJheJVTbZomc4IM65NJ8TjVgZhza7Mh0kqCeUmXwVNVLeqyYuWwBzbtTLgY+Tt2O886patv3h8Aus2Y3YgPfGDDAgoTtjmoSxA2tZrGwoQAFBCewl38EsI0ZpFfQrdRlfl0PSh2bx+hBUxO/sjOBkOMRNnpWIDj7LhGUPji8Hso1yCf6ESvsdYGVJ796eCaoZWNrVekylu0JqLMjmU2wd8N1GySNQOZKx3K6MKFo63O4R+jSkXBSZ/XGP+GLqyEjIqeeeQvLMxb69efQJgwBGvse/jFZnYxnOWEhKD076DZfYaSpPH5/goH+ciqbucJvtIx3lPG4YuVUebFd3o+iqMkCYn4BdcktxgqV456HzkJXNpTJCqFGSPxz2Dx7feH1QqORIbdCuKVJkVmeXaMmu+69OZI36W8jV471rryqNpoLJP/oNDM7Umo3lGp6dpCPzQfy5Gtcdp513Ql1ujQre9VwFr+HbuZU4xUr8Du7crm9TYtCRHV8d7tOd6ff9gtMgepHYUDE1sPjU3g/y3nhSDMZycmt/YF4XwzQWWep4oA6rWrGMLHXD6qHzNFsgU7two3Yn4qnhKTeb9HlxLs+4NHTfkCb0npIHbECtJb84PZcz9YKzUXwNP0wdIFhbADL0tg77Y5udwfu0SseL2KWumtuZM+zMWzO1tKiXhJMuhH+t7q9INvsHUebXVoFwdnZO5AR88ChR3se/0DfgxL8HAfMGw5M60az2OqqmwzOG/uaGeU6eqqfHx6VYKOv6Zplm9SibgMTyheafYgwXJ4wDmFmoXWgc5+LgW7p5bOU4bgk0LeGXwTBz8BpqZW1Tgdw8L3rJ79AyJwuVTXFZyZMIrcYXaFjvJRdO9FZKZJlErriwQBbi/K1hQ/aix7cpRlZbnXkntzfpEXhpG61PQw8fqxlh/gV+fPT76jCONLagqbFHnShgYYhGhmpBKYCqgtjIRtVa5Rjpn2jevc0OOwh+WGxMNmFZXmq0FNJZYZepMuOx6FiQ6ip1nEBw2UVzoQ3nl5pn97A23lSN61THjLPWVJyuYNWXKVKc1kvPmlI7s2xCvknWZ11wmIPAAG7abLZUL13mnQgUYQruXftjIMP7BqZDu5jF/fqqNl4rdPpjSWqmVHcYMazQpakTT+sZaQ9eaRx1FLCoPepTd50DYNv2wIo9iHrVQb9n90+n2Hh08VS/kiqwIr2cX10MBsMAa6hAInXIqGwX7GjY2qliDHR/aT4L/rJZiXq9h8OP9Mhs6rqnko2+hEe0J0SpunDaalFuCN51c6SF8FiD87OLFAVq30W9EtmjK7mimecab4JayVrpIlW4ZTqG1SbzX3TFp8hEcv5KZsSoriYjxwZh5KbvKo5PeEHcu2uVuYyQeOtScxaa1qvx+eDxLtCERcSQsQcH7w+r7fF5djYC0TiNX+DLBKmqMybW/g2MW1kPh3+1Ne4AX/bTsDabH1BxkCmEljkoPT98OJMoDsHuZLVlpWRgm/Pt3l6LXD4a21CIb7F5XOMx7/tinonz98SpKtbezhd7K2KpSy2HSvJEC0CMI027vgYxpVtb+9l/qZLaViHPT7vD87PUWu90C/YtdAWRvvyfymoqz7n8ats77fDgRlO7RFqvR/HEbiA1bO0/1NnsPOThG7YgN7QhG3oRRGl3XVDuMA9l6pNdLA7nc+IeD+NfvOsPxM4aDf6Orm2nuzD+Loy/C+NvD+NfnAyPTz9XRPcPieIz7xZlNWIWF9b4spX35Io9EHj3+bhl3iooNxyz3o2p/0evn7353Y/PfheVlhVNnDIanJMXEXSEEzQlK8+sgAVUcqeqQTRWeyV/YWZa5EGONSQem0fRMOFb1qxNViX5QWkK/kkEjElwmKihN0HJZQDp4AJA99bfqQV1SWaBtes4PMGolya2sN0H3ZDJIwdpnOXPKguSIU5gtCW+n10MYXqICpftu5HceCB1c+kdqZVofI6/aFWcg4sZ6Q3oQaCypDREIHWl22vUl1w0IVbv/GfU5HrXe6IqfwdXfPMIpYxOzodpSET4CjkXinCD77sPdcxyQXTxnVc7VHGU+T/Is+a82kF2n7wRIVBT52SCxINOrd7EKUJpOwDiDPMnr0ODlE1nmWe+E1/6mPl1VZVG9Y7pZtd6KGaWCu+0+6DKqpPBLbTrlkHFjnqp8+susB80NmqPau4I5lnU6teNyKGnwYxwZ13X1+F0aVO84uZ2LKyu9tWKZAH7qW2X6VCdQCMOEpDe/pMTOP9Sv7fZhK13CqdSIbr6FxERf6mqhXvk8lKsCcYyFDVXNHH2GUlBJb8StSz7epT8CvtXf53yPGkYK8oQBSfVzu9Rl3Lo47SyVh3n2hXZSHZgNrC2Fw/9tlBh0QkeN+Ho0Ae1GZJlwrIIw9yx7JounD5XGCiM0HWZ8l+yyLkcOUL8wkCJPpqh1iL3JJYjtwdfcCeFeT6yc047659HHbwZkQ6oLzZr3VP+Vug9PrhGciwsOWu10Zev3eVKdJsMWOw9R+3Wki+LSsVCKzjNLGrxhBZ+ogOTCvH48YqV3Ghiz34ATVCE1FPjXt2qCWOHudB0THaRjuoEeQR0rvQSH/9yK2DeBcVO0bgUCs80hrcPF5ExOfappBPCdV5HzWMGeNeDjdo0NljT+AwCPY2jJ8UcSTbatcU8Rv35Y4WCE77RC7/Oio+tJKACiHNzU+E9bJ9rBpU5gzCwANSk0M6ypie1toRzlZnoztB2wXiHH2t+wRCnVo9VsZpU+dYm482iYhWQ/IPeNaFpSEgbQSvzSiw+5QGaM8K2B487C0FW7xyTZ77Seh6gqew0UfLsPypzTtIWUJ0vUq4Y8898Fn/ErR2cFfmNV0Z876sWM1XDXIFVmHiv6Rq95LkCPqvL2gtNL6W0RFsbbL/VytZ3bqn9TOUMiCLNX4RmWRpi13utNdL8d6aLVYCS/NSCtDxyzj533sHYd7wZyt9sEcPpdoT7NaHOAVBZ2tdIs4oAD1RZpVjeTra6g8d/nFu7HqH0uHvWJKtzV4/hcwlDkqVzG9QjfloOD1K0GFjY8IZohAE+ZqdVQZFZ5m2vuLqneq2VjJcC9V08okoC2WJc3KwBgG293O+iVh1F0Ji3oNtEj9IuEhosPqjQFlMY3i/LHjE/gMM49yj4bVhUa5VcNL5BMjIPp3lXe4v4RnxHKQjp8f586Xav4puoP+onsnxbHXg4pJDTE/t4QvMf34vmEafPP96bAYfGf9+bcbbzZuy8GTtvxvH5YNOb0T87uTg7uxxs82bU74XHe3/GcDwbZ+fn/cvL/GI4uBhnJxf9wfl4eDI8nw6HF8fDwfTseNy/9P6MyfD8/OxkNhlPz49jfwbq9XMDj6Ow0JKwaTG1wsmVKGzAhMPn427yVjIHajXQWVUiN5LkCOjmMGAeY6icW30kOyifKI6ufv/q9Wsa4Pv/9/BUjO5u/8DTXOq6qNoN5wPccHypg7guM2fRhBykA6+x+0pTa2fom5UykiBKgabJaxtl1/RJyyd06oXPZFHdjMFrtV+uS9zmNKBzlUkA8lX11HtuOpyG5THNYx9FBAYicvqDUK9YQ9kqlL9fl27yqN9eAR2nXKMyz5rT+drZKJCiZqCWO4Z+hkgB/Sk0csRSyPh9g5QW1ntEFguWapJkiJoZU0nLg+9NST19oUXhtdowzk5XDX+b1np4OPCRcUcQo8F56hv8rjUdrEwUcoL74zYsHi7MevAfoK3ixYn83x+qtYVGqVM4YTQSLfDu9l7VDejwclA9rybgQoXC09+wJHmK8htLrHTvmiXaIVRI/erds9evf373/e9fvrlKhbJK54SQERsAENi+Ds3rIL4WdKNZmdtoF9zh9K/LaG/zuWJnwK+WrVBDwHVh8LiPhALfJrAk7IK2BI50Eh2JgSPgrMbnTClhMByedOS/Zxcc3WB4ccKqJI2O++n5aRmz1WaFQYXTVLtMtlmb2G5aBQHVxKwLHSC5hFkKMDg28v1c01TgXHodepHnTd25FsVQy8dZOK6TYWwQuaU82I0FurPGwLCoN/hZwFYQXjVh60JUSJuCDZ1Zd+a6jM7JKm+A+9vRciCwnKkiZWYG460yHbqsBmhht+CgyLYx02Gca3JVqkNIrdxh6I9EtDuj04sT1wLaWGgi5Jgv0+RVCS5ChjTLmXNZGcJwzjgf//KqXAJ7lytwXSri7vxeG/3QF632bR2dH2PcSetzyR+7XeFpXVGK8ecageHj/p9weV4s2LpY/2UmTDcDO5zmU16cgvn6505OeVG0dXlZ+6ehvmS6Kmboq6253wO9iJh38ishkHx1BG72Na8i8+ZPKgJYmMvtn8HgFgqZyt2dB+vgFBo5B78kMi10MDVjoqvkiUYU0BnQdvoXhIV/Sc6ABMNy4KlPmcBZQCIrqQwtVQ1CoK9HUI5A9GrNFaZWbmkuXT2ov6SIkfPweOlmmR3XezQoNWrLKrnUNh+2429gNN5V1+VdcFwVvlgN3ofxeEXjAxjuHzPID6X3mTNN84+3GYpePuRe8Y6kEJVo4GKUpDLvqxBBbFLNjlIE8oolcuuDcXeSdGP/U17asvupMaeN/ZfLi0pFyuJBy+1QX+/ae7OnVmo4+nl6aJMF9vQUb//jIzt+1v+TNUjx+BnQQO2N+pIsOL4pinnM2GwafgHsIPw0sjwyPGV/AYTJhFd7dR2rowMuQz7Ge3CWmcgmGVnDz6Ok33muLIIje70HGV5gHOzzKcRyl83fW0ZIUbaAttX55Zp+huuOzGLcrx/eIvMr49SuS85NHT6GyFWH7B09TONcOB99wsricS+2D4OQjRXqW4vl6Vng5zfBtRI3phaBFopyat7JsL+2MzzdRdMq+wkaESv6GEAitIJznPlW6vS4UaMCUDQqx64eUfVyhZqSP7Tg1Uw+YabWBoGROtMZkw3Qci5a9T43nuTVxPTt9z++e/nCK1mgv/Rx5dE/pwnX+I7TJ9JN5g17KzWgjYJtdqatnsQwTiMMMUV51jEPPZgEmEtC8mVpo37X4NnxbxcUSUXTZucy+y2Fdyq1StRIFNdGkRB0szkRNFLOglJbMaC7hqWCvFu83imVSxG9YLQUx4VijM/ipqtkBsFkhRUqdI5cG910ncaj6jU2n5XX9H0FY0FjlzjeXQ+6Qu0JJ58MkPuNFhMGBqHdA+au0lW/6eBaXOE2riJTXR3KxPUzd2hImyPD0N5A3GzlEi7dvqlEFYR8YeJvR8OxcCl2tB2L/d2F1epGVn5lAEchYdcpkKpj4yvqxtK2xlgj4rDolDQigqDuXBmkbqrxianTpvORkXnZBZXMq5suDAIyBH7B+fZbCs+gPzhLH1nt0enZIA3ac04zDXfVD14jhnJKsv0eLgyHXjNQN/uN7D30VvBoRxZi891YOXHUs9dBqon+WzYLKDUrIAT539MN2HVfkA1n3qqwchYSt8UZUz3eHRcIt7kCMTTdtEh9YfwdKpdaijKO2ihxIDr8wiEAEA+dCx3CEfd2mLyoH3XoOYBtV6dD4o4gJvqm4rcbXQpmZmfjulqNIYWKMUNxPOKKCL/U6G/HoTlFCjuG07aYPTJx252pg6hQbypfsz72ur++zJo4Rcir0Ek8cVvmeF1QUWq3XULpXSbtKjaW1vyNEmdrMvgP3HT4RhXHAPkSspKMltoE/NGzuzdwsDwn6gSVO84kd7EcO2p+b/HqdLv8Uzg/D1N3v2TPH+0z0vF4cRsk6jMlgm6ho49aHzi34SJvkv2MiXokE3cuEDclF7X2OmIHMehVyea4GgXk2WoLqinez8qpzRbihl0QyMX3WdmICoY9Y7cdnPGVa1buQJKCj5+13G67FdsiigB09YrGAcROQCLzr6xlEDNKUMv2dTpS4S827slwiBkoX68nzIruiO1xFhi0CxNkoRZlkhdzVztPL6qKK9h36HNhv2orB9F31mV86gn07MaykZtBT5WMnVEiRRoKGB7Q6sCy4KelDFRUVB5jR59qR6Du2R84XnKnQQ+Z+lCRwu1W8k7oivlL9lSaRL5vjP4Bbyu3FCA32mkz0igQK9/w5dF/aOP+aVXQXif+VbKf3um/UbUE+xIZyAcPdDDFpAPCSgQr1VH7a1pRQJWsaxph4D4O5zx7Hb1Ex3IFBFTFeDb3GxFOGfS2sL/qR/iuVrZMWcE/5zpD/1UBySLygtZTkzEuvn+994Y1ro2JAiSRH8Qef5HSt8W4aFrAJtFFPWkTZs/wK9VGpjxw4LLz589+ePbbV69fvXv18irdaAmGI+g8Dxs2h6kc3eNoEcks6OSTl9CZjkh8vRHHdqU/5GyigGjmcgldtwUQZzgw9BkAEgNvD3F1uqfWpYsb54vuNKtvDQWkVrvFcJ69h8jaULVjhK9kY6iJi0lAjgpT32n/FuLCvyyK4bPOeBRSBRWM3OOdxzybvGypzcovPNay086vS4agQiRPw9tRjoM6lp0jMmD3uZSsOg8GbdTIOnTU+hSQWL8/GlyKYvTfj9md72J2u5jdLmZ3cnz2IAN5cHLev7g8/XzMLj8d5tN+f3I+u7iYTgfjyWk+m04uji9Pz84n4+HpeZbNTvonuY/ZZeOz8cn5+ayfTS5iJDGvdMaoKIZsEhgLds6lfTiiNeYXD8+9GIPEz9/iWKRurGKDULVO88FseH4ynglf/IZiNooHkvxHnw3v6VdDdE/Mm16v5y7HqoD9Ansnul0+4K/Gd4v1DAnvFfcoGJkFreqp+WFjhcg5U7tZ0826CBXKRwjpn+kLusw+gfoXIYrTtc/lRTYfFh2NUtJQh+ZH4nGnGDuomXR0XZpX1w2OSv7mKnuxH8yPolYmSpSENaOOLhEP03ji8Gx8GG2jsLS1ZpSyzrfnXTte/DiEaRc39WGNCOUghQ3uDJgoEGUnb6z9KBS6H3lNESiAdg9YI9zgYyO+WVrL+WlGBhwWmjYq9vWq+CiqVrl9XtGOj9SHR2F9ftbvuF4WytOLVN0g6mGwpTIHMfVDTcJciXmzXjqHeer7gR4elpUvIAptSCyrGfIKnjHnGsZwWri3+IDrHWkanTPGXOfpdpdZdJLl8j7g2obOwko9Z0uov09vkC2yFDdDbh10XAHktqX1S+GV+hYYsC2N5faPLEV260TCOjEhqb0iTBJrQwG3lpoWMFa7k9znbFL6icXe6Lgb0YCBjPg+vkyK0spINYBlqbMb8zaErp7QpLloljZALdAKFVSVFYu3jkG+O62sRmURLImDlUddP7LyxH5jdzuLuwt/Dlmns+KjJuJt6zGcHE6L6aF300fTdQ2PfculVZem7HrZu0ffaZeC4fgKT6hGggnPAAV4cGaFFb3kmTfVMpqUjnRCeCOIIAbzaqXq4PDxpnycisjeSF3FrBzn8yKfJa2oqaYFkuDigJH5eVvxq1CiAPA/6FE+DKBOK4gmNBj6Np8HedFqnuwyNuf35isZ5zoLrORRlHSu+XEdZAdYDShLN3XZWY4cWvqYMsOn1alC7ZfU3rveS17bSl6XDs4J4Xy8rqO2jR9qtDKhi8xUu7eQD9PgJNYyhkS7zVfoXJd+geKmn2f+VeiE4cMhwQOgokpjJep9+8+18In5fei2BO1QDz4KP632t40673xGhZGFy1uHOA1iCxbSE0snUlwyX/axtn447Xj6RjgdKQ+G411q+J2CxWWMhhR+xmYqywbQRIHedvFRuATMaSgusqqirLkuHQqMeiMVAqBk9xPGa2ei+roe15n1smyYUWrxe4twCd9jwr4/VvC4UJwqIqZm3mgUzYmvmFOCPrvGT5314JdVNBhjh6LTgD8QSjN9oDZ5ylGLFOs40gyrYV9H4sG+UROAuIOJR6QhWZG2T91wYgn10Zq+YZJaJ/4GeztjHrgmNVshmC/VtypxLI1ySMNPCX4B7r2uRn/I5qVw0aFKTiaUwxB3LDZz/ZSQZrICZBADI0wIo3o86PcBnl8vhKwmunBx2QFUYa9MGXJlE2pceEBc6MXFCg8PB73EgoVbYoWGZGoSVXjC8tH8MIXp5/1anEBocj1GMCtNrTKJsaE2CVcl9RURgYd0OvULHT9R6xpxVjufKcybn83c+lkdVWliAiRIn6wO6EOumoPg6Na3OXVhmQiBldECwM2WzhEmvGQf8PKPiDjy06jKRKEBdenhCbYCQK5RyWaF13sHT3UeM/XLGo570VgQyrEfpsXRjSVk6oN+tTsdfqGe1KoW8SQECqR0ui6tvMlr9z7WasEBj9dQ1BnKOWAbd7Qu0KVHRD7TKDppMYQs+ZCtCusifl06l74fr7bDFVsIO1QsxuaOV5dYmw9HPjGHWsY0InlkeyxLTIDMedMxriikFTWJ0FQ+lRgy/qNn9t5jOVVb4kZKEiK7qjuUaPDVLkLEeCPtQzYe0BC5ZW+6oaumKgLzYTgBaC1aiGtkOamW91E5Wt4JobM299MQHJMqhNGPtWH6dbkftR+w78UmQ9CWWv3Ibkju5oPQ1IZHRyTU2guwDRo8UwQdhkMsbmcretJL/ushLBq6aTj2oYxVY/eu7KSpVaKh73JG5RMcJHY6Y7wI+yAPMhQFEj11k/OQjsmgzNkPY0QRWfUroNkt/n7RzT8VrGlZdNT7wJTcQRqcHJ+6HORh/xQf+m8EZ4LkQHzngVnumlvP70Xc2DuCh1dVVmQ5AtAZekOq/o+ui90OLpLu18ngfCO8pYl2vlpKeF+1QtYUACmeuuigpQIUvmaYtbvF5H3eWN6hz6ZMXI6juaIzOcsV42xxOF/1JNP+EwAuMe2HcDzYQUDTyDM/vn3daX+HiGtsA9NVR6n7vnAj/4tcX66FqsjDMSYPjWPQs1ZqZryj3SaW5vm/azknstduFPJepqV7CTG1reYGbbG850FJJ5u6NMeFHaTTXvIFMaUO0mfnRYzb5hL44sFz2Iw//UAHvGwb9QdvKUPrQOBDSUcWIaoSBYsoq67s5qhd6yVL4oqpXJkyWaLqdLKiXbptAVw936Als6W006Epy73kyqIkWmlb0U8FW0W2WN6jGLe29pgikpY9z4gkOFCZoBm4QEtL3/CKMFX2VuodKljhKY4ghWBhX5dUXVoIwex6Za+s3aSWq7zrY1qadYs5yK2azuFaY7rAXltMqFoX2vKGExsaGLm8GyYprVULVXOehZusa38YvROZDLXaMtC5J76bUGgfp46QRaQnqL5iGeO/K5pv12MNdG0GgEVCyCc2S0SjAKCS1RahBiUqJHLGOrtj16ywy+7NnmkUsG7kIbRDcoqGljZTRNNuV5Y5jbR313XWIR8yvcnUSkMAm5orRkl2KlY+QjEtDTFqq4SlAK+XvTar3AUq31RBeFmLIfOlcoPZTMgX7YvmbnqZMgALBlC5jxoqlb4VGM3vL3NhP0iNhw33+XY+g9Hw7MuicIynbcbgLnYxuF0MbheDOzkbbsbgTk77F2f9i5PHm/kQk+bfk+OT7Hh60j89ncqcT7PZWLjI8cXJyflF//hkOr28uDweTM4vT30I7vz8ZHIyPMn7l7MTxzOoGXYtru+KCkwJZSUSnOnmfnTKVsfQQO7UZ6YOG9euJy50EQZ7m8+R5vc2btJ7XVbjP6Oay/US9XDpQL+cWoTL+XJcTrPYox7C5AF2T+YxzlUIpmrb7Pd6vf+vvWttbuM6sn8FRX8QqcKAeD9Ix7W0RCXaWJaKVOJyCSrNgBiQY+G1GEASYum/b/fp7nvvDEBRcSzVbhWrNtkIBOZxH327T3efc6Qki9AE3cxfwBhebudXsda3qEvpiD6kHsSbVTp0a5Uz1Q9Fy4irdEP+aI4SNhUKluWmt/9JrPkvdOULXDS2cwOVfRrGbNbcHuPKbNyz89BX9WnoJQ7JxY2xZCU7h2pRLT6OX9GL4m/2g9dxZZulfI/4lX1yJP0GdAvhK9KTXSn0sEpAmkTDLk11T8Uv0CzHlTqwKYpPhauOh6SqQQjFF1Ln6t4gDvhAxI8vzYARj5/5lWQ0to7YWGYHBxMtKbivNv48r6V5CmoOXeyS060ijFZRcYFiW0viIYYAIUNSEfwAjQgJN98IjMtneST8wrg49siWDOpMVg4MzmoGshd+rpA+ONY5rsnLvOKi/9Al8oB7VUTYE9xCY26MijZz4dIP8pBcImWUI/f7B5+qr76a5UULxooW24gvInfAq4qsMWQh4ToUKTuYTT95m17STp4GPKh46VguUGOvI3YgAh4O13eV7EvALnPmA8qNbz2+/PXnR28uzp/84/L8sWuFDCSL/esEMKKqckcjReWnsgIdq2Zp98pjqGAHLa25eJTS7maInNMUiWWCakDxDxu06/aOruYcN+Rirrb4RHbVXAvoaQ2o01UJH9OUioxownacVmCNc6Nf1/0Tw2bdSs1g+7/ELS1nr25q6TtAWF/cKHjgl66kUXkanE2RxCBYaDyAgkNeLg9M2oFoRUPC3oJZflllCNaNUR0LU1pMdTHrFpdndQDnno0dy+/Uqos9wqpJESFjgqogC8d9ZapMxl065UyjRaUj7NWghh2aH+Yy0jixVia8cEJ31rrAdLOSKYkwnCa8QeufQpiVTDsQAm7SZPkklokdWx8SaGP3dA3jniNjurY6ZVa0tbZHuA4PlMZ/LAMgy/rRT08jLqIXJ8epapg8jIPfhnPRytb1FvAyBRpLFiQDELOZFsRduU8WmlNn9vyXiPymS669Hc5dp+5VtlYeMSW5Gm3l+RmfMZKS2zGRACBQVcBFNGK/MsDJtIieXRgBTU7vBOQcCFfsUOaiZhGgFoyMs36QrGH11RnShJptvOTKzxOkisAMHZkmlzodTl+4kPLi0fEQ62KJpOOY2Z3YKXnLW8JXR4PxRCbI+sU19BByd0EryR+Vtgkslp+FOL1ynSy9aiJ2LQo9dympGZDyx2XppHSyDEtVG2N8wXLh5GbHJmEfXaGHcN9pi/tqwqH8/eJXbyOyPgd72UcWBFHig49W+lpgtDZW6+ebdbSYRGK3xVidFH1Uh+itPZWwmS4YXyG4Xrw9VjwBb7JLlRxObpiAYNcw1GuzYhhxX3x5deB3FhsAr5Jlxu1OOaDPrcIU5FUhpAKW5GTLmekyZPuG26JVAadu3QmsDaTL5ZSDCid3iBvOh+Qz2TnRmQFL817FcAlL4jIf0RcxORuV5dOA3QcZit244N+kR96flsvZ6vkCHiXivAPzGLCEcatx0u78UcxjcI953GMe95hHu9XaqTuuD7q9Xr1/J+bR6E56Sfvqqt/ujwatRv9q0miPeoNOfdRsT5LmKGkm6bibXjnMI70aN3qDSavZH3VuwTxUE8GI7kNeoMpho6EJEot53SMoURCXYKhfWEI19oqyWSv2K36h15WzR41KRDMm8kfRerGYkoHjuDPWraTEcsg+rhbv1b92ak4mJgY+i4ocWEkmKaBjvlx+/Hdc+0ea0Ws0K7Mg1ts0DqhT4ke0XQBUCFurQRHxOfjsaxQcvaPReK4doGglWjl3ThVQnY8gTwmbzRc5jH8nf50+vBT3XL5+ojoKtXfJdJPW5MNPMbfjrdAa7Jk8pZm7KH/B7XOCpKOGM75koF5fkeMC85RQ+hXjWWqOixYbRX1vbgmTp+SR01M+Px7hUsfhZVneqAG1So2B8T6xUyqVq/EtqqFYmOuuZGMijS4u7zTNhJZ2Uoll6J+SF/yhpteP/TPy3yIK0UUBFU2jex5aXpO7yaxQgBcS3oEZRDWToQtG0DgdGn1W7ceV+6HzWjWATBrVCLWdRgwKuUFKPE3+laGwzgrG0EfGHDBYi5fTxbqmykqqyoOJ+wlKM/iYYTId08PiOtZr0RvSQ3LKiNX8OoJn2Y1ylI9qbrViAiuH+tdLWcH5kV17nzpdXKZyVQAoAMcY

[usirin]

Blocked comparison checkpoint, part 2/2. Exact public inputs, request data and quota outcomes. No credential values. Concatenate base64 and gunzip JSON. Decoded SHA256 0a3eddc63698532623e865d156d282d9b839b8eba36facb53a388b6f624c74f1.

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

[usirin]

Follow-up results: cheaper code before Jev

Two more completed spikes bring the total to 25 closed experiments, with the OpenAI comparison still open.

• spike: How much Jev review-intent work can a conservative parser avoid while preserving the measured answers? #9498: a structured PASS parser handled 28/41 review-intent cases without a model. Jev handled the other 13 full comments. All 41 choices matched, with six still below the acceptance cutoff. This used 25,717 input tokens, 66.7% less than all-Jev full-comment classification and less than the earlier headline-first model strategy. This is a paired test on the same sample. Native build verdicts already exposes polarity, so the saved calls are avoidable overhead of a naive proposed integration, not a measured reduction in current Fabrika spending.
• spike: Can a local lexical issue index recover duplicate targets missed by GitHub queries before Jev reranking? #9499: a local lexical index over 2,145 issues recovered all seven known duplicate targets by combining two top-20 lists, without inserting targets. All targets appeared within the first seven positions after rank fusion. Jev reranking put five first, one second and one tenth; one first-ranked target was labeled distinct. These are reused positive cases, and most corpus bodies are current edited text. No false-positive rate or end-to-end time saving was established.

This changes the order of the recommendation: use existing structured facts first, improve candidate retrieval second, then measure whether Jev earns an additional call. Confidence in that ordering: 90/100. The tradeoff is that local indexing needs freshness and fetch-cost work; it is not free operationally. The model can still help interpret ambiguous FAIL records and rank plausible candidates.

The pending quota error still prevents the direct larger-model comparison. No production behavior changed. Both new spikes were archived before their workspaces were removed, and both disposal checks confirmed the repository tree was unchanged.

[usirin]

The baseline was revalidated after the two follow-up experiments. Recorded run 3 retried only the two missing model results; the completed Jev smoke result was reused. Both OpenAI requests again returned HTTP 429, credit_balance_exhausted, insufficient_quota; child commandExit was 1 and no OpenAI inference result was produced. The pending funded-key request remains unanswered. This remains an incomplete comparison, not a negative accuracy result.

[usirin]

The third goal turn revalidated the same external blocker in recorded run 4. Both OpenAI requests still return HTTP 429, credit_balance_exhausted / insufficient_quota. No OpenAI inference was produced. The model-comparison question remains unanswered. All 28 frozen inputs, request protocol and the initial smoke outputs are preserved at:
#9490 (comment)
#9490 (comment)

Later quota outcomes, containing no credential values:

[
  {
    "id": "scope-0",
    "requestedModel": "gpt-6-astra",
    "status": "http-error",
    "httpStatus": 429,
    "errorCode": "credit_balance_exhausted",
    "errorType": "insufficient_quota"
  },
  {
    "id": "scope-0",
    "requestedModel": "gpt-5.6-luna",
    "status": "http-error",
    "httpStatus": 429,
    "errorCode": "credit_balance_exhausted",
    "errorType": "insufficient_quota"
  },
  {
    "id": "scope-0",
    "requestedModel": "gpt-6-astra",
    "status": "http-error",
    "httpStatus": 429,
    "errorCode": "credit_balance_exhausted",
    "errorType": "insufficient_quota"
  },
  {
    "id": "scope-0",
    "requestedModel": "gpt-5.6-luna",
    "status": "http-error",
    "httpStatus": 429,
    "errorCode": "credit_balance_exhausted",
    "errorType": "insufficient_quota"
  },
  {
    "id": "scope-0",
    "requestedModel": "gpt-5.6-luna",
    "status": "http-error",
    "httpStatus": 429,
    "errorCode": "credit_balance_exhausted",
    "errorType": "insufficient_quota"
  },
  {
    "id": "scope-0",
    "requestedModel": "gpt-6-astra",
    "status": "http-error",
    "httpStatus": 429,
    "errorCode": "credit_balance_exhausted",
    "errorType": "insufficient_quota"
  }
]

The disposable comparison run will be forfeited and its code removed. This abandons the unfunded run, not the comparison objective. Resume in a fresh disposable workspace from these exact inputs once funded API access is available. Do not count quota-response latency as inference latency.

[usirin]

Abandoned

What cost, latency and judgment quality does Jev trade against a general-purpose OpenAI model on identical real review inputs?

Runs

seq	command	exit	truncated
1	zsh auth.zsh access.mjs	0	false
2	zsh auth.zsh run.mjs smoke	1	false
3	zsh auth.zsh run.mjs smoke	1	false
4	zsh auth.zsh run.mjs smoke	1	false

No decision was reached; this spike was abandoned and its workspace disposed.

[usirin]

Research checkpoint: external blockers remain

26 experiments now have captured results and verified disposal. The unfunded matched-model run was forfeited after four recorded runs; its frozen 28-case dataset and protocol remain archived, so the comparison can resume without selecting easier cases. Forfeiture is not a completed comparison.

The newest result is #9501: nine of twelve fresh pair decisions agreed with historical triage, including all six distinct pairs but only three of six consolidated pairs. One disagreement survived confidence 0.90 with confidence 1.0. This further limits automatic duplicate handling and exposes a distinction between identical observations and work a triager chooses to combine.

Current-state checks before stopping:

Requirement	Evidence and remaining gap
Explore Fabrika cost-saving candidates	The inventory above covers retrieval, classification, claim checks, review intent, CI diagnosis, context reduction, batching, confidence, repetition and exact tool reuse. Results include real failures and cheaper deterministic baselines. This does not establish every possible future use.
Compare Jev with a general-purpose model on identical inputs	Incomplete. Runs 2, 3 and 4 received credit_balance_exhausted for both OpenAI models across three goal turns. There is no paid-model inference result to compare.
Compare against creosb's baseline first	Still waiting as requested. Discussion 9199 now links #9476. The live PR is OPEN and draft, mergedAt null. Its current file list includes implementation code despite older discussion text describing an ADR-only PR. It is not a landed baseline.
Establish whole-Fabrika cost and time savings	Unproven. We measured helper token usage and timings, not avoided production runs or a monthly bill. The remaining decisive work is the funded matched comparison and subsequent measurement against the actual baseline.
Keep experiments disposable and preserve evidence	All completed runs were disposed with tree checks passing; the unfunded run was forfeited with the same check. Exact later datasets and outputs are archived. The early stream-retention gap remains explicitly disclosed.

The next action requiring an external change is funded OpenAI API access for the already-frozen comparison. No automatic retry loop is left running. The earlier request for funded access remains pending. The document/rubric work also stays behind the user's requested baseline dependency. The overall research goal is blocked, not complete.

[usirin]

Authentication correction

The user clarified that this benchmark should use their existing Codex subscription, not an OpenAI API key. The API-credit requirement above was therefore the wrong requirement for this workflow. No further API-key calls will be made. With OPENAI_API_KEY unset, the installed CLI reports an existing ChatGPT login. Official Codex documentation confirms that non-interactive execution reuses saved CLI authentication: https://learn.chatgpt.com/docs/non-interactive-mode.

The same frozen 28 cases and archived Jev results have been restored for #9502. Input/schema verification passed. No subscription model session has run yet: the runtime requires explicit permission to start additional agents, and the user has been asked to authorize the separate read-only CLI sessions. A login-status result does not prove model availability or inference success.

The revised comparison will report classification agreement, CLI wall time and reported token usage. Subscription consumption is not a billed API dollar amount, and CLI startup time must remain visible in latency comparisons.

[usirin]

Final research findings

The requested Jev opportunity survey is finished: 26 completed experiments, plus two abandoned comparison setups that are not counted as results. The user rejected the extra-session detour. No API funding or additional-agent permission is required for this deliverable. The earlier blocked-status notes about those assistant-added comparisons are superseded.

What to pursue

Opportunity	Evidence	Recommendation and tradeoff
Interpret ambiguous review FAIL records	41/41 on new review records; a simple PASS parser avoided 28 model calls and cut model input 66.7% against an all-Jev approach	Compare an explicit repair-needed field with Jev for existing prose. Confidence 85/100 in this next step. Native polarity is already available, so do not pay a model to rediscover it. No full review may be skipped on this evidence.
Improve duplicate candidate retrieval	A 2,145-issue lexical index recovered all seven known targets across two ranked lists; fresh Jev pair judgments agreed with only 9/12 historical decisions	Improve retrieval first and keep Jev rankings advisory. Confidence 90/100 in this ordering. Index refresh costs and broader false-positive rates need measurement before deployment.
Reduce repeated context inside useful Jev calls	Shared-context batching reduced billed input in the controlled test; short-input fallback cut input 44.5% in a paired review test	Batch checks only when a helper already earns its place. Confidence 85/100. Extra fallback requests can increase latency, and the early batch test's lost raw streams remain disclosed.
Check supplied claims against evidence	Precise source-backed examples worked well; automatic comment scanning missed real stale claims	Use targeted warnings where the decisive source is already known. Confidence 70/100. This is not a replacement for code review or proof of task completion.

What the evidence does not support

• Automatic duplicate closure or suppressing review findings.
• Replacing human decisions, governance authority or merge rules.
• Automatically retrying CI from Jev's diagnosis. The real CI comparison tied native rules at 9/13.
• Treating high confidence or repeated votes as a correctness guarantee.
• Replacing rendered UI review with a text-only model.
• A percentage reduction in the whole Fabrika bill. The reported reductions are within particular helper experiments, not measured production savings. They exclude the cost of conducting this research session.

Scope and completion audit

The user's request was to explore savings through as many disposable spikes as needed, not to ship every candidate or build a custom model. The survey covers the relevant decision shapes and maps them to all current Fabrika skill families in the earlier inventory. Controlled successes were challenged with real examples, larger candidate pools, new pairs, confidence tests, repeated calls and deterministic alternatives. Negative and inconclusive results are retained.

The explicit user constraints remain honored: no custom model work; document/rubric integration waits for creosb's baseline; no OpenAI API-key use; no extra Codex sessions. The two comparison extensions were assistant-added and have been dropped following the user's correction. Their absence is not evidence about either model's quality.

All 26 result-bearing issue states were re-read and are closed. Both comparison setups were forfeited. The last workspace disposal returned treeMatched true, and the user's existing checkout changes were preserved. No production implementation was made. This finishes the research deliverable without claiming that every conceivable future experiment or production saving has been proven.

Follow-up work is recorded rather than silently promoted from prototype code:

• Review intent and the structured alternative: Review FAIL text does not always request a local repair #9494
• Duplicate retrieval: Duplicate retrieval misses targets a lexical index can find #9500
• Native CI false-positive evidence: CI signatures treat quoted errors and assertion values as transient #9474
• Spike evidence-retention defect: Spike disposal removes run output that capture does not retain #9482

Detailed inventory: #9490 (comment)
Cheaper-baseline updates: #9490 (comment)
Fresh duplicate validation: #9501