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Review Aspect Low-Confidence Review Queue
x402
Review Aspect 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 naive-Bayes aspect classifier (performance/appearance/value/other) and returns a prioritized queue of records needing human review.
Price / call$0.01
Success—
StatusUnknown
How this service works
Review aspect - Low-confidence review queue: Low-confidence review queue for Review aspect. 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 filtered and prioritized list of submitted records where the classifier's predicted probabilities fall below confidence thresholds, indicating which records most need human review. Includes record IDs, text, actual vs. predicted labels, and probability scores for the flagged items.
Example use cases
E-commerce review annotation QA
“I've got 120 labelled product reviews where my naive Bayes model predicted aspect categories like performance and appearance, and I have the probability scores for each. Can you build me a low-confidence review queue so my annotation team knows which ones to manually verify?”
Classifier validation before deployment
“Before I deploy my aspect classifier, I want to flag all the uncertain predictions in my test set — I have the actual labels, predicted labels, and probabilities for each of 80 records across performance, appearance, value, and other. Can you generate the low-confidence queue for me?”
Active learning sample selection
“I'm doing active learning on aspect classification and I need to know which of my 200 unlabelled predictions are least confident — I have the predicted aspect and four class probabilities for each record. Can you pull the low-confidence ones into a review queue so I can prioritize what to label next?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-review-queue?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of aspect-classified text records (up to 200) with class probabilities from a local naive-Bayes or small model and need to identify which predictions are uncertain enough to warrant human review. Ideal for human-in-the-loop annotation workflows, active learning pipelines, or pre-deployment validation of aspect classifiers targeting performance, appearance, value, and other categories. Not suitable if you need full model training, hosted inference, or foundation-model fine-tuning.
Side effects
- Records array exceeds 200 items — request rejected or truncated
- Probabilities array does not contain exactly 4 values — validation error
- Predicted or actual label not in allowed enum (performance, appearance, value, other) — schema error
- Empty records array — returns empty queue or error
- Malformed probability values outside [0,1] range — validation failure
- Payment of 0.01 USDC not completed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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