Set release boundaries for the threshold prediction game

Operational risks for the threshold tuner on five labeled scores center on treating the five-row accuracy as a production SLA, plus the earlier failure mode (threshold outside 0..1, or reporting accuracy without TP/FP/TN/FN). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

sla={'acc':0.8,'n':5}
if sla['n']<100: print({'publish_as_sla':False,'reason':'n too small'})

Expected evidence: publish_as_sla False. A failed assertion means stop, investigate, and do not publish the threshold prediction game.

Name who can disable the feature, what data is retained, and how to roll back to the last known good artifact. Pin the reviewed configuration (versions, thresholds, allowlists) so “what shipped” is reconstructable for prediction-game.

Write the release blocker as a predicate, not a feeling: “Do not ship the threshold prediction game if treating the five-row accuracy as a production SLA.” Pair it with a passing control that shows the reviewed configuration still works for 5 (truth, score) pairs. Name an owner and a rollback handle (git tag, docs_version, previous image).

Security pages must not paste the happy-path demo. If your gate code looks like the implementation page, replace it with a deny/allow check aimed at treating the five-row accuracy as a production SLA.

State the data retention rule in one line (what is stored, for how long, who can read it). Then state the kill switch (env flag, config pin, or feature owner). The threshold prediction game is not shippable without both, even when confusion matrix sums to 5; accuracy matches hand count looks healthy.

At the safety and operations stage for prediction-game, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about 5 (truth, score) pairs that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: confusion counts computed by hand at threshold 0.5.

For this page specifically, success looks like an executable deny gate for the lab-specific threat while still centering the user decision to choose a cutoff that balances errors without claiming generalization from five rows. If you cannot point to a file, command, or assertion that proves that for the threshold prediction game, stay on this page instead of advancing.

Confusion matrix glossary

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