Review & export¶
Start with a sorted warning report:
Keep the same guidance, fields, and exemplars as your first run to reuse its cache. Only reporting options change here.
Read the scores¶
Each field receives a NORMAL / ANOMALY classification, the full label
probability distribution, and Jev's confidence. All field questions for an entry
are batched into one request.
| Result | Meaning |
|---|---|
Field score |
Probability of the anomaly label |
Entry score |
Maximum field anomaly probability |
Entry label |
Chosen label of the highest-scoring field |
warning |
Whether the entry score meets or exceeds the threshold |
The combined score is a review priority, not a calibrated probability that any field is wrong. Confidence is a separate API value; it is not used as the score.
Adjust the review threshold¶
The default threshold is 0.5. Changing it does not trigger fresh inference.
The threshold determines warnings independently of the chosen label.
Pick an output format¶
One JSON object per entry, with nested field results. Includes id, source,
label, score, warning, fields, model, assessed_at, request_hash,
cached, and usage. Each field includes its path, value, label, probabilities,
confidence, and anomaly score.
Output files must be distinct from the input, Python config, guidance document, exemplars, and assessment cache. Both preview and scan reject symlink or hardlink aliases of those files before opening the output.
assessed_at is the original assessment timestamp. usage on cached results
describes the original call and does not represent new token usage.
Streaming and incomplete runs¶
By default, results stream as entries complete. --sort-score buffers results
and emits them only after a successful scan. If a scan fails, stderr reports
INCOMPLETE and the command exits with status 1. Streaming output may contain
partial results; sorted output will not contain the buffered rows. Successful
assessments remain cached in either case.
See shell pipelines for scripts that check exit status before using the output.