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Waste Monitoring IQR Outlier Flags
x402
Waste Monitoring 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 2026-10-19).
Applies interquartile range (IQR) outlier detection to waste monitoring records (collected_kg or recycled_kg) and flags anomalous rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Waste monitoring - IQR outlier flags: IQR outlier flags for Waste monitoring. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded iqr outlier flags deliverable.
Output
Returns row-level outlier flags for each submitted waste record, indicating which rows have collected_kg or recycled_kg values that fall outside the interquartile range, along with IQR-derived bounds used for classification.
Example use cases
Flag anomalous weekly collection totals
“I have 80 weekly waste collection records by material type with collected_kg values — some readings look suspiciously high or low. Can you run IQR outlier detection on the collected_kg field and tell me which rows are flagged as outliers?”
Quality check recycled kg data before reporting
“Before I submit our quarterly recycling report, can you check these 120 recycling records for outliers in the recycled_kg field? I want to catch any data entry errors or abnormal values using IQR analysis.”
Spot unusual waste events by material
“We had a data spike in our waste monitoring logs last month — can you analyze these 60 records grouped by material and flag which collected_kg readings fall outside the IQR bounds so we can investigate?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-analytics-waste-monitoring-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local IQR-based outlier detection on structured waste monitoring records (up to 200 rows) without needing external enrichment, forecasting, or ML models. Ideal for data quality checks before reporting or pipeline ingestion.
Side effects
- Missing required fields (row_id, material, date, collected_kg, recycled_kg) causes validation error
- Exceeding 200 row limit returns a 400 error
- Invalid date format (non-YYYY-MM-DD) fails schema validation
- Insufficient numeric variance in data may yield no flagged outliers
- Null values in the selected numeric field may be excluded from IQR computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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