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River Level Observations — String Normalisation + Two-Measure Correlation
x402
River Level Observations — String Normalisation + 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 2026-11-02).
Normalises station group strings and computes correlation between river level (level_m) and flow (flow_m3s) measurements across up to 200 supplied observation rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
River level observations — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied river level observations records (level_m, flow_m3s). 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 analytical reports: (1) normalised station group strings for each input row, and (2) a correlation analysis between the level_m and flow_m3s measures across the supplied dataset, computed entirely from the provided data with no external lookups or predictions.
Example use cases
Flood monitoring data quality check
“I've collected 80 daily river level and flow readings from our gauging stations over the past three months — the station names are inconsistently formatted. Can you normalise the station group labels and tell me how correlated the water level is with the flow rate across those records?”
Catchment hydrology research summary
“I have a CSV of 200 river observation rows with station group names, dates, level_m, and flow_m3s values for our catchment study. Please clean up the station group strings and compute the correlation between river level and flow for us.”
Environmental compliance data preparation
“Before submitting our river monitoring data to the regulator, I need the station group names standardised and a statistical correlation report between the level and flow columns — I have 120 rows covering the last quarter.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-07-033?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular river observation data (up to 200 rows) and need both string normalisation of station group labels and a statistical correlation between level and flow in a single call. It is a self-contained, offline analytical operation — ideal when you do not need real-time gauge lookups, external data enrichment, or predictive modelling, and want a low-cost ($0.01 USDC) reproducible computation on your own supplied data.
Side effects
- Exceeds 200-row limit — request will be rejected; split into smaller batches
- Missing required fields (row_id, station_group, date, level_m, flow_m3s) — validation error returned
- Malformed date format (must be YYYY-MM-DD) — schema validation failure
- All level_m or flow_m3s values are null — correlation cannot be computed
- row_id values not unique — may cause ambiguous output rows
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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