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Field Survey Coverage Data Quality Pipeline
x402
Field Survey Coverage Data Quality Pipeline is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Audits field survey coverage records for quality issues, normalises strings, and fills missing values in surveyed_m2 or target_m2 fields across up to 200 rows, returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Field survey coverage — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied field survey coverage records (surveyed_m2, target_m2). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports covering: (1) a record quality audit flagging completeness, format issues, and anomalies in the survey records; (2) a string normalisation report showing standardised station_group and other string fields; and (3) a missing-value fill report showing which surveyed_m2 or target_m2 nulls were imputed and with what values (zero, mean, or median). All computed locally with no external lookups.
Example use cases
Pre-analysis survey data cleaning
“I have 120 field survey records with station_group labels, dates, and surveyed_m2 and target_m2 values — some surveyed_m2 entries are missing. Can you audit the record quality, normalise the station group strings, and fill those missing surveyed_m2 values using the mean method so my data is ready for analysis?”
Ecological coverage completeness check
“Before I submit our seasonal survey dataset can you check the record quality and flag any issues in our surveyed_m2 and target_m2 columns, normalise any inconsistent station group names, and fill any nulls in target_m2 using zero fill? We have about 80 rows.”
Survey pipeline data validation
“Run a full data quality pipeline on these 200 field survey coverage rows — I need the record quality audit, string normalisation on station_group, and median imputation for any missing surveyed_m2 values, and give me all three reports.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-22-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have field survey coverage data (with surveyed_m2 and target_m2 measurements) that needs a combined data quality audit, string normalisation, and missing-value imputation in a single call returning three structured reports. It is ideal for pre-processing survey datasets before statistical analysis, with no need for external data, predictions, or live lookups. At $0.01 USDC for three reports on up to 200 rows it is cost-effective for batch data cleaning pipelines.
Side effects
- More than 200 rows submitted — request rejected with maxItems validation error
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, station_group, date) trigger required-field errors
- Numeric values outside ±1,000,000,000,000 range cause schema rejection
- Invalid method enum value (not zero/mean/median) causes validation error
- Invalid field enum value (not surveyed_m2/target_m2) causes validation error
- Empty rows array (minItems:1 violation) returns validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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