# Study Recruitment Row-Change Analysis & Missing-Value Fill

> Study Recruitment Row-Change Analysis & Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Computes adjacent-row changes and fills missing values for study recruitment data (eligible_candidates, enrolled_candidates) across up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-09-162?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/study-recruitment-row-change-analysis-missing-value-fill-4dec927f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Z_umfU4kAm2Aq9Z0T_w02

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 study-recruitment-row-change-analysis-missing-value-fill-4dec927f -d '<json body>'
```

Example prompt: I have study recruitment records for two course groups over four dates — can you compute the adjacent row changes and fill any missing eligible_candidates values using median imputation? Here are up to 200 rows with row_id, course_group, date, eligible_candidates, and enrolled_candidates.

## When to prefer this

Use this endpoint when you need both sequential row-change analysis and missing-value imputation on study recruitment data (eligible_candidates and enrolled_candidates) in a single low-cost call. It is purpose-built for educational or clinical recruitment datasets with a date+course_group structure and supports up to 200 rows. Prefer it over general-purpose analytics tools when you want a fast, cheap, deterministic computation with no external data dependencies.

## Known failure modes

- More than 200 rows submitted — request rejected
- Invalid date format (must be YYYY-MM-DD) — validation error
- Unknown fill method value outside zero/mean/median enum — error
- Missing required fields (row_id, date) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Empty rows array — minimum items constraint violation

## How this service works

Study recruitment — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied study recruitment records (eligible_candidates, enrolled_candidates). 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 independent analytical reports: (1) an adjacent row change report showing the delta between consecutive rows for the specified field, and (2) a missing-value fill report where nulls in the target field are replaced using the chosen method (zero, mean, or median). Both reports are computed locally on the supplied data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "course_group",
     "date",
     "eligible_candidates",
     "enrolled_candidates"
    ],
    "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
     },
     "eligible_candidates": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "enrolled_candidates": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "course_group": "group-a",
     "eligible_candidates": 10,
     "enrolled_candidates": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "course_group": "group-b",
     "eligible_candidates": 20,
     "enrolled_candidates": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "course_group": "group-a",
     "eligible_candidates": 15,
     "enrolled_candidates": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "course_group": "group-b",
     "eligible_candidates": 30,
     "enrolled_candidates": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "eligible_candidates",
    "enrolled_candidates"
   ],
   "type": "string",
   "default": "eligible_candidates"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/study-recruitment-row-change-analysis-missing-value-fill-4dec927f/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)