# Numeric Precision Comparisons — Adjacent Row Changes + Secondary-Measure Weighted Mean

> Numeric Precision Comparisons — Adjacent Row Changes + 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 2026-10-23).

Computes adjacent row-to-row changes and a secondary-measure weighted mean for up to 200 rows of numeric precision comparison data (source_value vs. transformed_value).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-159?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/numeric-precision-comparisons-adjacent-row-changes-secondary-measure-5cef6a54
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_zl8GFxeGR-qOqoX2v6lvk

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 numeric-precision-comparisons-adjacent-row-changes-secondary-measure-5cef6a54 -d '<json body>'
```

Example prompt: I have a dataset of numeric precision comparisons with source and transformed values across up to 200 rows — can you run adjacent row change analysis and a secondary-measure weighted mean on the source_value field for these records?

## When to prefer this

Choose this endpoint when you need two specific offline statistical analyses — adjacent row changes and weighted mean — on numeric source-vs-transformed comparison data in a single call. It is ideal for data quality audits, ETL validation, and transformation accuracy checks where no live data lookup or external enrichment is needed. Prefer it over generic analytics services when your data fits the precision-comparison schema (source_value + transformed_value per row) and you want a cost-effective bundled report ($0.01 for both analyses).

## Known failure modes

- Input rows array is empty or exceeds 200 items — rejected with validation error
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) on any row — rejected
- Date field does not match YYYY-MM-DD pattern — validation error
- Numeric values outside [-1000000000000, 1000000000000] range — rejected
- Invalid field enum value (must be source_value or transformed_value) — rejected
- All values null for the selected field — weighted mean may be undefined or zero

## How this service works

Numeric precision comparisons — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean for supplied numeric precision comparisons records (source_value, transformed_value). 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) adjacent row changes showing the difference between consecutive source_value or transformed_value entries per the selected field, and (2) a secondary-measure weighted mean that uses one numeric column to weight the average of another — all computed locally on the supplied data without external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-adjacent-row-changes-secondary-measure-5cef6a54/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)