# Training Question Low-Confidence Review Queue

> Training Question Low-Confidence Review Queue is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).

Identifies low-confidence predictions in a labeled training dataset of educational text records classified as concept, procedure, example, or assessment, returning a review queue for human inspection.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-review-queue?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-question-low-confidence-review-queue-41a0809c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_S2vRS4vvLp7k-hZbaeTR6

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 training-question-low-confidence-review-queue-41a0809c -d '<json body>'
```

Example prompt: I have up to 200 labeled training records for my question classifier — each with an ID, text, actual label (concept, procedure, example, or assessment), predicted label, and four class probabilities — can you run them through the low-confidence review queue so I can see which ones my model is most uncertain about and need human review?

## When to prefer this

Choose this endpoint when you have a small labeled text dataset (up to 200 records) classified into the four educational categories (concept, procedure, example, assessment) and need to identify which records a naive-Bayes model is least confident about for human review — particularly useful before retraining or during annotation quality control. Not suitable for large-scale datasets, foundation model training, or classification domains outside educational question types.

## Known failure modes

- Records array exceeds 200 items — request rejected or truncated
- Probabilities array does not contain exactly 4 values — validation error
- Probability values outside [0,1] range — schema validation failure
- Invalid label values outside the enum (concept, procedure, example, assessment) — rejected
- Empty records array returns empty review queue
- Payment not processed (x402 protocol failure) — endpoint not served

## How this service works

Training question - Low-confidence review queue: Low-confidence review queue for Training question. 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 low-confidence review queue deliverable.

## Output

A bounded deliverable containing a review queue of up to 200 text records identified as low-confidence by a naive-Bayes baseline, including the flagged record IDs, texts, actual and predicted labels, and probability distributions that triggered the low-confidence threshold — intended for human review and relabeling.

## 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": [
       "concept",
       "procedure",
       "example",
       "assessment"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "concept",
       "procedure",
       "example",
       "assessment"
      ],
      "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": "concept sample document number 0 for this supplied dataset.",
     "actual": "concept",
     "predicted": "concept",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-1",
     "text": "concept sample document number 1 for this supplied dataset.",
     "actual": "concept",
     "predicted": "concept",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-2",
     "text": "concept sample document number 2 for this supplied dataset.",
     "actual": "concept",
     "predicted": "concept",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-0",
     "text": "procedure sample document number 0 for this supplied dataset.",
     "actual": "procedure",
     "predicted": "procedure",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-1",
     "text": "procedure sample document number 1 for this supplied dataset.",
     "actual": "procedure",
     "predicted": "procedure",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
 
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-question-low-confidence-review-queue-41a0809c/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
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