# Sampling Observations — Exact Row Deduplication + Base-100 Series

> Sampling Observations — Exact Row Deduplication + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Deduplicates exact rows and computes a Base-100 indexed series for up to 200 sampling observation records (sample_value or sample_weight).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-047?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-exact-row-deduplication-base-100-series-55d48b5b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_xPP6jVuWtS-b11PkJZpNo

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 sampling-observations-exact-row-deduplication-base-100-series-55d48b5b -d '<json body>'
```

Example prompt: I have a dataset of sampling observations with up to 50 rows — each row has a row_id, dataset_group, date, sample_value, and sample_weight — can you deduplicate exact duplicate rows and compute a Base-100 index series on the sample_value field, using row index 0 as the base?

## When to prefer this

Choose this endpoint when you need both exact-duplicate row removal and a Base-100 normalization of a numeric field (sample_value or sample_weight) from sampling observation records in a single low-cost call. It is ideal for pre-processing survey or sample datasets before further analysis, especially when you want to anchor values to a baseline index point. Prefer it over alternatives when your data fits within 200 rows and you need both deduplication and indexed series outputs together rather than separately.

## Known failure modes

- Exceeding 200 rows causes a validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- base_index out of range (>= number of submitted rows) causes an error
- Missing required fields (row_id, dataset_group, date) causes schema validation failure
- All rows are duplicates leaving an empty deduplicated set
- sample_value or sample_weight value exceeds allowed magnitude bounds

## How this service works

Sampling observations — Exact row deduplication + Base-100 series: Exact row deduplication + Base-100 series for supplied sampling observations records (sample_value, sample_weight). 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) a deduplicated version of the submitted rows with exact duplicate records removed, and (2) a Base-100 indexed series for the selected field (sample_value or sample_weight), normalized relative to the row at the specified base_index. Both reports are computed locally with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "sample_value",
     "sample_weight"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "sample_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "sample_weight": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "sample_value": 10,
     "dataset_group": "group-a",
     "sample_weight": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "sample_value": 20,
     "dataset_group": "group-b",
     "sample_weight": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "sample_value": 15,
     "dataset_group": "group-a",
     "sample_weight": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "sample_value": 30,
     "dataset_group": "group-b",
     "sample_weight": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "sample_value",
    "sample_weight"
   ],
   "type": "string",
   "default": "sample_value"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-exact-row-deduplication-base-100-series-55d48b5b/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)