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Training Question Low-Confidence Review Queue
x402
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.
Price / call$0.01
Success—
StatusUnknown
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.
Example use cases
Pre-retraining data audit
“Before I retrain my question classifier, run these 150 labeled records through the low-confidence review queue — each one has an ID, the text, the actual label, predicted label, and four class probabilities — and show me which ones scored low confidence so I can fix the labels first.”
Catching mislabeled procedure examples
“I've got 80 training samples tagged as either concept, procedure, example, or assessment with their predicted labels and probability scores — can you pull the low-confidence review queue so I can see which procedure and concept records my model is mixing up?”
Quality gate for new annotation batch
“My team just finished labeling 200 new training questions as concept, procedure, example, or assessment — run the low-confidence review queue on them with their predicted labels and probabilities so I can flag anything uncertain before adding it to the training set.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-review-queue?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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