# Commerce Intent Low-Confidence Review Queue

> Commerce Intent 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 2026-10-11).

Identifies and surfaces low-confidence predictions in a commerce intent classifier (browse/purchase/return/other) for up to 200 labelled text records, using local naive-Bayes baselines and evaluation tools.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-commerce-intent-review-queue?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/commerce-intent-low-confidence-review-queue-317fc578
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yPzlSEpkrkq3AXydhAZqj

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 commerce-intent-low-confidence-review-queue-317fc578 -d '<json body>'
```

Example prompt: I have 200 labelled customer query texts with their actual and predicted commerce intents (browse, purchase, return, or other) plus the 4-class probabilities for each — can you run them through the low-confidence review queue to flag the uncertain predictions I should manually check?

## When to prefer this

Choose this endpoint when you have up to 200 pre-labelled commerce intent records (browse/purchase/return/other) with class probabilities and want to identify low-confidence predictions for human review or model auditing — particularly useful in ML evaluation pipelines where no hosted model is needed and local naive-Bayes baselines suffice. Prefer this over full retraining or foundation-model endpoints when the goal is targeted QA on uncertain predictions rather than generating new labels.

## Known failure modes

- Records array exceeds 200 items — endpoint rejects or truncates input
- Missing required fields (id, text, actual, predicted, probabilities) on any record causes validation error
- Probabilities array not exactly length 4 or values outside [0,1] causes schema rejection
- Empty records array returns empty review queue with no useful output
- Text field exceeds 1000 characters per record causing truncation or rejection

## How this service works

Commerce intent - Low-confidence review queue: Low-confidence review queue for Commerce intent. 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 the low-confidence review queue: a curated set of records from the submitted batch where predicted commerce intent probabilities fall below confidence thresholds, along with local dataset evaluation metrics and naive-Bayes baseline comparison results for browse, purchase, return, and other classes.

## 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": [
       "browse",
       "purchase",
       "return",
       "other"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "browse",
       "purchase",
       "return",
       "other"
      ],
      "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": "browse sample document number 0 for this supplied dataset.",
     "actual": "browse",
     "predicted": "browse",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-1",
     "text": "browse sample document number 1 for this supplied dataset.",
     "actual": "browse",
     "predicted": "browse",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-2",
     "text": "browse sample document number 2 for this supplied dataset.",
     "actual": "browse",
     "predicted": "browse",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-0",
     "text": "purchase sample document number 0 for this supplied dataset.",
     "actual": "purchase",
     "predicted": "purchase",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-1",
     "text": "purchase sample document number 1 for this supplied dataset.",
     "actual": "purchase",
     "predicted": "purchase",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-2",

… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/commerce-intent-low-confidence-review-queue-317fc578/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)