# River Level Observations — String Normalisation + Two-Measure Correlation

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-07-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/river-level-observations-string-normalisation-two-measure-correlation-b3db5eb8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ORfRGH7R60TsDlZAt4-Yu

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 river-level-observations-string-normalisation-two-measure-correlation-b3db5eb8 -d '<json body>'
```

Example prompt: I have 150 rows of river observation records with station_group labels, dates, river levels in metres, and flow rates in cubic metres per second — can you normalise the station group strings and run a correlation analysis between the level and flow measurements?

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "level_m",
     "flow_m3s"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "level_m": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "flow_m3s": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "level_m": 10,
     "flow_m3s": 1,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "level_m": 20,
     "flow_m3s": 2,
     "station_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "level_m": 15,
     "flow_m3s": 3,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "level_m": 30,
     "flow_m3s": 4,
     "station_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/river-level-observations-string-normalisation-two-measure-correlation-b3db5eb8/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)