# Meeting Topic Label and Prediction Audit

> Meeting Topic Label and Prediction Audit 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).

Audits classification labels and predictions for meeting topic text records across four categories (planning, progress, decision, review), returning evaluation metrics and diagnostics.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-label-audit?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/meeting-topic-label-and-prediction-audit-69918779
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1YJhxcdb2hBWrucoFY0SP

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 meeting-topic-label-and-prediction-audit-69918779 -d '<json body>'
```

Example prompt: Can you run a label and prediction audit on these 50 meeting transcript snippets I've labeled as planning, progress, decision, or review — I want to see accuracy metrics and where the classifier went wrong, including predicted probabilities for each record?

## When to prefer this

Choose this endpoint when you have a small labeled dataset (up to 200 records) of meeting-topic text and want a local, lightweight audit of classifier quality without sending data to a hosted model or foundation model. Ideal for NLP pipeline QA, training-data validation, or benchmarking a naive-Bayes baseline on the specific planning/progress/decision/review taxonomy. Not suitable for large-scale batch evaluation or training new models.

## Known failure modes

- More than 200 records submitted — payload exceeds limit
- Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error
- Probabilities array does not contain exactly 4 values summing near 1.0 — schema rejection
- Invalid enum value for actual or predicted outside allowed set — rejection
- Empty records array — no audit can be performed
- Payment not included or insufficient — x402 payment required error

## How this service works

Meeting topic - Label and prediction audit: Label and prediction audit for Meeting topic. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded label and prediction audit deliverable.

## Output

Returns a structured audit report covering classification accuracy, per-class metrics (precision, recall, F1), confusion matrix statistics, probability calibration diagnostics, and comparisons against naive-Bayes baselines for the four meeting topic categories: planning, progress, decision, and review.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "records": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "id",
     "text",
     "actual",
     "predicted",
     "probabilities"
    ],
    "properties": {
     "id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "text": {
      "type": "string",
      "default": "",
      "maxLength": 1000
     },
     "actual": {
      "enum": [
       "planning",
       "progress",
       "decision",
       "review"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "planning",
       "progress",
       "decision",
       "review"
      ],
      "type": "string"
     },
     "probabilities": {
      "type": "array",
      "items": {
       "type": "number",
       "default": 0,
       "maximum": 1,
       "minimum": 0
      },
      "default": [],
      "maxItems": 4,
      "minItems": 4
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "planning sample document number 0 for this supplied dataset.",
     "actual": "planning",
     "predicted": "planning",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-1",
     "text": "planning sample document number 1 for this supplied dataset.",
     "actual": "planning",
     "predicted": "planning",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-2",
     "text": "planning sample document number 2 for this supplied dataset.",
     "actual": "planning",
     "predicted": "planning",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-0",
     "text": "progress sample document number 0 for this supplied dataset.",
     "actual": "progress",
     "predicted": "progress",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-1",
     "text": "progress sample document number 1 for this supplied dataset.",
     "actual": "progress",
     "predicted": "progress",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/meeting-topic-label-and-prediction-audit-69918779/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)