Measure confidence-gated routing on labelled data

01 · Role in the system

What Jev does here

Jev answers one classification question per row, and its confidence drives a cascade that escalates only uncertain rows to a larger fallback model. On 500 Banking77 examples, routing at threshold 0.67 reached 80.2 percent accuracy for about 0.103 dollars per 500 decisions, beating either model alone; on 500 Web of Science abstracts the same routing tied Jev alone for 46 percent more money. The protocol was frozen before results were read, and the raw JSONL, figures, and analysis code are committed.

02 · Control boundary

Where Jev sits

Per-row typed answers with a confidence threshold to an escalate-or-accept rule; code measures coverage, accuracy, latency, and cost against the frozen protocol.

Code owns the loop, permissions, thresholds, validation, and side effects. Jev owns only the bounded judgments described above.

03 · Known limits

What this evidence does not prove

• The two datasets produced opposite outcomes; no threshold, accuracy-gap sign, or cost result transferred between them.
• Web of Science labels come from publication metadata rather than per-document annotation, so part of the measured error is label ambiguity.

04 · Attribution

Public sources

This is a Community record: the project was published by a third-party community author.

• FirasSX914 ↗Community · github · public · checked 2026-09-18