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Label Review Analytics: Exact Row Deduplication + IQR Outlier Flags
x402
Label Review Analytics: Exact Row Deduplication + 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).
Deduplicates exact rows and flags IQR-based outliers in label review records containing reviewed_records and changed_labels fields, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Exact row deduplication + IQR outlier flags: Exact row deduplication + 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
Two independent analytical reports: (1) an exact row deduplication report identifying which rows are exact duplicates across all fields, and (2) an IQR outlier flags report identifying which rows have values in the selected field (reviewed_records or changed_labels) that fall outside the interquartile range bounds. Both reports are derived locally from the supplied data with no external lookups.
Example use cases
Annotation pipeline data quality audit
“I have a batch of label review records from our annotation pipeline — each row has a row_id, dataset_group, date, reviewed_records, and changed_labels. Can you run exact row deduplication and flag any IQR outliers in the reviewed_records column so I can clean up the dataset before training?”
Detecting anomalous label change spikes
“We track changed_labels daily across multiple dataset groups and I'm seeing some suspicious spikes. Can you take my label review rows and flag any IQR outliers in the changed_labels field, plus remove any exact duplicate rows that crept in?”
Pre-release dataset consistency check
“Before we hand off this label review dataset to the model team, I need to confirm there are no exact duplicate rows and no statistical outliers in reviewed_records. Here are about 150 rows with row_id, date, dataset_group, reviewed_records, and changed_labels — please run both the dedup report and the IQR outlier analysis.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-049?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local, self-contained data quality checks on label review records specifically involving reviewed_records and changed_labels metrics. It is ideal for MLOps and annotation pipeline workflows where you want both deduplication and outlier detection in a single call without sending data to an external prediction or enrichment service. Prefer this over general-purpose analytics APIs when your dataset is under 200 rows and you need deterministic, reproducible results with no side effects.
Side effects
- Input exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, reviewed_records, or changed_labels) cause validation errors
- Null numeric values in the target field may reduce outlier detection accuracy
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Insufficient data points for meaningful IQR calculation (very small row counts)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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