# Data Collection Completeness — Numeric Row Ordering + Secondary-Measure Weighted Mean

> Data Collection Completeness — Numeric Row Ordering + 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 2026-11-08).

Computes numeric row ordering and secondary-measure weighted mean analytics for data collection completeness records (observed vs expected values), producing 2 reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-22-121?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-08
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-collection-completeness-numeric-row-ordering-secondary-measure-c988a897
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6V-yzOvwlEBBjnpe-LyDT

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 data-collection-completeness-numeric-row-ordering-secondary-measure-c988a897 -d '<json body>'
```

Example prompt: I have a set of data collection completeness records for two course groups across early October 2026 — each row has an observed value and an expected value. Can you rank the rows numerically and compute a weighted mean using expected_values as the secondary measure for up to 4 rows?

## When to prefer this

Choose this endpoint when you need both numeric row ordering and a secondary-measure weighted mean on data collection completeness records (observed vs expected) for up to 200 rows in a single call. It is ideal for education or training data quality workflows where you want two analytical perspectives in one cheap call without any external data dependency. Prefer alternatives if you need histograms, correlation, pivot tables, or more than 200 rows.

## Known failure modes

- Exceeding 200 rows causes rejection — trim input to ≤200 rows
- Missing required fields (row_id, date, observed_values, expected_values) cause validation errors
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Null values in both observed_values and expected_values for a row may skew or break weighted mean calculation
- Invalid field enum value (not 'observed_values' or 'expected_values') causes error

## How this service works

Data collection completeness — Numeric row ordering + Secondary-measure weighted mean: Numeric row ordering + Secondary-measure weighted mean for supplied data collection completeness records (observed_values, expected_values). 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 2 analytical reports: (1) a numeric row ordering of the submitted records ranked by the selected field, and (2) a secondary-measure weighted mean calculated across the rows using the complementary measure as weights — all computed locally on the supplied data without any external lookup or live data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-collection-completeness-numeric-row-ordering-secondary-measure-c988a897/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)