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Research Sample Recovery – IQR Outlier Flags
x402
Research Sample Recovery – 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 2026-10-16).
Applies interquartile range (IQR) outlier detection to research sample recovery mass data, flagging anomalous records among up to 200 supplied specimen rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Research sample recovery - IQR outlier flags: IQR outlier flags for Research sample recovery. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.
Output
A bounded IQR outlier flags report covering each submitted row, indicating which records fall outside the interquartile range for the selected mass field (input_mass_g or recovered_mass_g), along with the computed quartile boundaries used for flagging. No enrichment or forecasting is included — purely local descriptive analytics on the supplied data.
Example use cases
Lab QC check on recovery yields
“Here are 50 rows of specimen recovery data from last month's extraction runs — run IQR outlier detection on the recovered_mass_g field and flag any records that look statistically anomalous.”
Identify bad batches in material study
“I've got 120 rows of input and recovered mass measurements across two specimen groups, group-a and group-b — can you flag which rows have outlier input_mass_g values using IQR?”
Pre-analysis data cleaning for sample set
“Before I run my main analysis, I need to screen these 80 research sample records for outliers — use the IQR method on recovered_mass_g and tell me which row IDs I should investigate or exclude.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-sample-recovery-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 labeled specimen recovery records and need a fast, local IQR-based outlier flag report with no external data enrichment. It is ideal for lab QC pipelines, pre-analysis data cleaning, and research workflows where you want to identify anomalous mass measurements without sending data to an external ML or forecasting service. Prefer alternatives if you need forecasting, trend analysis, or outlier detection on non-mass fields beyond this schema.
Side effects
- Fewer than 1 row submitted returns a validation error
- More than 200 rows submitted exceeds the maxItems limit and is rejected
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, input_mass_g, or recovered_mass_g) cause per-row rejection
- All-null values in the selected field may produce undefined IQR bounds
- Non-numeric mass values cause type validation errors
- Payment not included or insufficient USDC results in 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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