# Temperature Observations — Exact Row Deduplication + Secondary-Measure Weighted Mean

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-01-051?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/temperature-observations-exact-row-deduplication-secondary-measure-2dda08f4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lSwr4jXHAmbgjSBlaWvB9

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability temperature-observations-exact-row-deduplication-secondary-measure-2dda08f4 -d '<json body>'
```

Example prompt: I have a set of temperature observation records with columns for row_id, station_group, date, temperature_c, and observation_count — can you deduplicate exact duplicate rows and compute the observation-count weighted mean for temperature_c across up to 200 rows?

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "temperature_c",
     "observation_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "temperature_c": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observation_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "station_group": "group-a",
     "temperature_c": 10,
     "observation_count": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "station_group": "group-b",
     "temperature_c": 20,
     "observation_count": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "station_group": "group-a",
     "temperature_c": 15,
     "observation_count": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "station_group": "group-b",
     "temperature_c": 30,
     "observation_count": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "temperature_c",
    "observation_count"
   ],
   "type": "string",
   "default": "temperature_c"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/temperature-observations-exact-row-deduplication-secondary-measure-2dda08f4/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)