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Label Review — Numeric Row Ordering + IQR Outlier Flags
x402
Label Review — Numeric Row Ordering + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Sorts label review records by numeric value and flags statistical outliers using the interquartile range (IQR) method, producing two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Numeric row ordering + IQR outlier flags: Numeric row ordering + IQR outlier flags for supplied label review records (reviewed_records, changed_labels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent reports for the submitted label review rows: (1) a numeric row ordering report that ranks rows by the selected field (reviewed_records or changed_labels), and (2) an IQR outlier detection report that flags rows whose values fall outside the interquartile range bounds. Both reports operate on the same submitted data with no external lookups.
Example use cases
Outlier detection in annotation volume
“I have 50 label review records across two dataset groups from the past month — can you order them by reviewed_records and flag any rows that look like outliers using IQR? I want to spot which annotation sessions were unusually large or small.”
Ranking changed labels by frequency
“Can you sort these 30 label review entries by changed_labels count and identify any statistical outliers using IQR? The data has row IDs, dates, and group names — I want to see which sessions had abnormally high label change rates.”
Data quality audit before model training
“Before I feed this label review data into training, can you run numeric ordering and IQR outlier detection on the reviewed_records field for these 80 rows? I need to catch any suspicious entries that could skew my model.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-119?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a ranked ordering of label review rows and IQR-based outlier detection in a single $0.01 call, without requiring any external data sources or ML predictions. It is ideal for data quality audits of annotation pipelines, pre-training data checks, or monitoring label review session volumes. Prefer this over general-purpose analytics when your data conforms specifically to the reviewed_records/changed_labels schema and you want two distinct statistical reports in one request.
Side effects
- Fewer than 1 row submitted — minimum of 1 row required
- More than 200 rows submitted — batch limit exceeded
- Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) in any row
- Invalid date format — must match YYYY-MM-DD pattern
- Field enum value not recognized — must be reviewed_records or changed_labels
- Null values in the selected numeric field may affect IQR computation
- Payment of 0.01 USDC not completed — x402 payment required before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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