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Reservoir Observations — String Normalisation + Secondary-Measure Weighted Mean
x402
Reservoir Observations — String Normalisation + Secondary-Measure Weighted Mean 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 a capacity_m3-weighted mean of stored_m3 (or vice versa) across up to 200 reservoir observation rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Reservoir observations — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied reservoir observations records (stored_m3, capacity_m3). 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) a string normalisation report cleaning the station_group field values, and (2) a secondary-measure weighted mean report computing the weighted average of the selected field (e.g. stored_m3) using the other measure (e.g. capacity_m3) as the weight, across the supplied rows grouped by station_group.
Example use cases
Water utility monthly storage summary
“I have 120 daily reservoir readings from three station groups with stored_m3 and capacity_m3 columns — please normalise the station group labels and give me a capacity-weighted mean of stored_m3 so I can summarise monthly storage levels.”
Environmental audit of reservoir fill rates
“Can you clean up the station_group strings in my 80-row reservoir dataset and calculate a weighted mean of stored_m3 using capacity_m3 as the weight? I need this for an environmental audit report across sites.”
Irrigation planning data quality check
“I've got reservoir observation data for 4 groups over the past quarter — about 90 rows — and the station names are messy. Normalise the strings and compute the capacity-weighted average stored volume so I can plan irrigation allocations.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-19-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have reservoir observation data (stored volume and capacity in cubic metres) and need both string normalisation on categorical fields and a secondary-measure weighted mean computed locally — without any external lookup, prediction, or live data fetch. Ideal for batch analytical pipelines on up to 200 rows where reproducibility and low cost matter.
Side effects
- Missing required fields (row_id, station_group, date, stored_m3, capacity_m3) cause validation errors
- Submitting more than 200 rows triggers a payload size rejection
- Null values in both stored_m3 and capacity_m3 for a row may cause incomplete weighted mean output
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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