Review deterministic confidence, source reliability, relevance, direction, freshness, price, policy, and user-entered paper-order checks in TEMIRIN.
August 3, 2026 · 4 min read
TEMIRIN stores one deterministic confidence value from the watchlist intelligence brief beside source reliability, relevance, direction, freshness, policy status, and explicit evidence IDs. It is workflow context calculated from stored fields, and reviewers can inspect those inputs before relying on the value.
Keep reviewer conviction in a decision note. A user may disagree with the deterministic context after reading the source and market resolution rule, and the record should preserve that human reasoning without rewriting the stored input.
A percentage without provenance can look more authoritative than it is. Review the underlying source tier, reliability, relevance, direction, freshness, canonical link, and explicit market identifier. Then compare the first-recorded trigger price with the latest current price and observation time.
TEMIRIN shows spread and visible depth as timestamped public market context beside the user-entered paper request. Missing or stale values remain explicit so the reviewer can record that limitation in the decision note.
The investment-policy surface stores a stake cap, source and category enablement and max-stake overrides, confidence settings, a free-capital percentage, a price-move guard, and a kill switch. The decision endpoint enforces the price-move guard, and the kill switch affects opportunity eligibility.
Paper orders separately enforce plan-scoped per-order and rolling twenty-four-hour notional limits, per-market exposure, and portfolio concentration. Confidence does not bypass a blocked state and TEMIRIN checks the user-entered paper-order notional against the implemented limits.
A reviewer can use confidence context when deciding whether to watch, reject, approve in the app, or create a paper order. Each action remains a separate, attributable record, and any paper notional is entered explicitly by the user.
If the current price moves beyond the configured guard, the in-app endpoint rejects the stale decision conditions and returns the record to human review. The earlier review timestamp stays preserved beside the refreshed evidence and price context.
Use the same field names in alerts, exports, metadata, and support copy. Describe the deterministic confidence inputs, link to the source record, and keep the user-entered paper amount and returned policy results in their own clearly labeled fields.
Sample records from several categories and verify that the displayed confidence context, evidence IDs, trigger price, current price, policy reason, and reviewer note can each be traced to their stored origin. If an input is unavailable, label it unavailable instead of inventing precision.
The value of confidence context is disciplined comparison across review records, with the stored inputs and human note available for inspection.
A useful review exercise compares records with similar deterministic confidence context but different source reliability, freshness, direction, price movement, and public-wallet exposure. Ask reviewers to explain why they watched, rejected, or created a paper order while keeping the stored value separate from the user-entered amount. Preserve the note and check that exports use the same narrow definition. If two users reach different conclusions, retain both human reasons instead of modifying the deterministic field after the fact. This makes later comparison honest and helps support identify confusing labels. It also protects the product boundary: confidence is inspectable workflow context, while every real venue decision remains solely with the user.
Keep a terminology checklist beside releases that touch confidence display. The checklist should confirm deterministic input fields, explicit market identity, separate human notes, and consistent current policy and paper-order labels. This quick editorial check prevents a harmless workflow percentage from turning into a public prediction claim.
Choose three watchlist records with different source reliability and freshness. Reopen each canonical source, verify its explicit market identifier, compare trigger and current prices, and confirm that the displayed confidence value still traces to the stored deterministic inputs. Have a second reviewer read the same fields and add an attributable note when interpretation differs. Finally, create one small user-entered paper request and one intentionally blocked request so the exported records show confidence context and enforced paper checks as separate facts.
The review exposes deterministic fields such as source reliability, evidence relevance, direction, freshness, and the recorded market context. Reviewers can inspect those inputs instead of relying on an unexplained score.
No. Confidence context is one visible input, while the user chooses the simulated notional subject to current paper-order and policy checks. The value is not advice and does not override configured limits.
Keep the conflict and timestamps visible so the reviewer can challenge relevance and reliability before deciding. Price context, source quality, and hard policy checks should remain separately inspectable.
