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Elevation Samples — Record Quality Audit + Two-Measure Correlation
x402
Elevation Samples — Record Quality Audit + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Audits record quality and computes two-measure correlation between elevation_m and sample_weight for up to 200 supplied elevation sample rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Elevation samples — Record quality audit + Two-measure correlation: Record quality audit + Two-measure correlation for supplied elevation samples records (elevation_m, sample_weight). 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
Returns two independent analytical reports: (1) a record quality audit identifying issues such as missing values, out-of-range fields, and malformed records across the supplied rows; and (2) a two-measure correlation report quantifying the statistical relationship between elevation_m and sample_weight across the dataset. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Pre-analysis data quality check for elevation survey
“I have 150 rows of elevation sample data from our field survey, each with a row ID, station group, date, elevation in meters, and sample weight. Can you audit the record quality and check the correlation between elevation and sample weight before I hand this off to the modelling team?”
Validating sensor station measurements
“We've collected elevation and weight readings from multiple station groups over the past month — can you run a quality audit on these records and tell me how correlated the elevation_m and sample_weight values are across all 80 rows?”
Detecting data issues in elevation benchmark dataset
“I need to check this elevation benchmark dataset for bad or missing records and also get a correlation analysis between the elevation_m and sample_weight columns. There are about 60 rows with station group labels and dates — can you produce both reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-13-015?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 elevation sample records and need both a record quality audit and a pairwise correlation analysis between elevation_m and sample_weight in a single low-cost call. It is ideal for pre-processing validation before downstream modelling, detecting data quality issues in elevation survey data, and understanding the statistical relationship between two numeric measures without any live data lookup or external enrichment.
Side effects
- Exceeding 200 rows results in rejection or truncation
- Missing required fields (row_id, elevation_m, sample_weight, date, station_group) causes validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Null or extreme numeric values may degrade correlation quality
- Empty rows array returns an error
- Malformed JSON payload causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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