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Research Sample Recovery – Explicit Missing-Value Fill
x402
Research Sample Recovery – Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-13).
Fills missing numeric values (input_mass_g or recovered_mass_g) in up to 200 research sample recovery records using zero, mean, or median imputation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Research sample recovery - Explicit missing-value fill: Explicit missing-value fill 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 explicit missing-value fill report.
Output
A bounded missing-value fill report showing which rows had null values in the specified field, what imputed values were assigned using the chosen method (zero, mean, or median), and local descriptive analytics summarizing the fill operation across up to 200 records.
Example use cases
Lab data gap filling before analysis
“I'm running a sample recovery study and several of my input_mass_g readings are missing. Can you fill those nulls using median imputation across my 150 specimen rows so I can proceed with analysis?”
Zero-fill for recovered mass anomalies
“Some of my recovered_mass_g entries are null in my research dataset — I want to treat them as zero rather than drop the rows. Can you apply zero fill across my 80 sample recovery records?”
Mean imputation QC report for specimens
“Before I submit my lab report, I need to impute missing input_mass_g values using the mean across my specimen groups. Can you process my 60 research sample recovery rows and give me a report of what was filled?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-sample-recovery-fill-missing?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 typed research sample recovery records with missing numeric mass values and need a quick, local imputation report using standard statistical methods (zero, mean, or median) without external enrichment or forecasting. It is purpose-built for lab/research specimen workflows and outputs a bounded fill report rather than a full ML-based imputation pipeline.
Side effects
- Empty rows array or fewer than 1 item returns an error
- More than 200 rows exceeds the batch limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Field value not in enum ['input_mass_g','recovered_mass_g'] is rejected
- Method value not in enum ['zero','mean','median'] is rejected
- All values in the target field are non-null, resulting in no fills performed
- Mass values outside the allowed numeric range cause validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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