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Temperature Observations — Exact Row Deduplication + Secondary-Measure Weighted Mean
x402
Temperature Observations — Exact Row Deduplication + 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 2026-10-24).
Deduplicates exact duplicate rows and computes observation-count-weighted mean temperatures from supplied temperature observation records, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Temperature observations — Exact row deduplication + Secondary-measure weighted mean: Exact row deduplication + Secondary-measure weighted mean for supplied temperature observations records (temperature_c, observation_count). 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
Two analytical reports: (1) a deduplicated version of the input rows with exact duplicate records removed, and (2) a secondary-measure weighted mean of the specified field (temperature_c or observation_count) weighted by observation_count, computed across the supplied data. Results cover up to 200 input rows and are derived entirely from the submitted data using local analytical engines with no external lookups.
Example use cases
Clean weather station duplicate readings
“I've got daily temperature readings from multiple station groups with some exact duplicate entries — can you remove the duplicates and give me the observation-count weighted mean temperature for these records? Here's the data with row_id, station_group, date, temperature_c, and observation_count fields.”
Weighted average temperature by observation volume
“I need to aggregate my temperature sensor logs where each row has an observation_count — can you compute a weighted mean of temperature_c using observation_count as the weight, after deduplicating any identical rows?”
Data quality check on temperature dataset
“Before I use this temperature dataset in my report, can you run exact row deduplication and produce a weighted mean summary for temperature_c weighted by observation_count? I have about 150 rows covering several station groups and dates.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-01-051?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw temperature observation records that may contain exact duplicate rows and need both a cleaned dataset and a statistically sound weighted mean that accounts for differing observation volumes per row. It is ideal for pre-processing weather station data before downstream analysis, especially when observation_count varies significantly across records and a simple average would be misleading. It operates entirely on supplied data with no external dependencies.
Side effects
- Input exceeds 200 rows — request rejected
- Missing required fields (row_id, station_group, date, temperature_c, observation_count) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- All rows are duplicates — deduplication returns single representative row
- Null values in temperature_c or observation_count may affect weighted mean computation
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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