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Task Type Low-Confidence Review Queue
x402
Task Type 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 and surfaces low-confidence predictions from a naive-Bayes task-type classifier for human review, given up to 200 labelled text records with actual/predicted labels and class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Low-confidence review queue: Low-confidence review queue for Task type. 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 review queue deliverable listing up to 200 records identified as low-confidence by the naive-Bayes classifier, with associated metadata (IDs, text, actual vs predicted labels, probabilities) to guide human review and relabelling.
Example use cases
QA audit of task classifier output
“Here are 150 records from our task-type classifier — each has the text, the actual label (research, writing, analysis, or implementation), the predicted label, and the four class probabilities. Can you generate a low-confidence review queue so our QA team knows which predictions to manually inspect?”
Detecting borderline research vs analysis labels
“Our naive-Bayes model keeps confusing 'research' and 'analysis' tasks. I have 80 labelled records with actual labels, predicted labels, and probability scores — can you pull a low-confidence review queue so I can see which ones are most ambiguous?”
Pre-training data cleaning for task classifier
“Before we retrain our task-type model, I want to weed out the uncertain samples from our 200-record training set. Each record has the text, actual and predicted labels, and four class probabilities — can you run the low-confidence review queue to flag the ones we should relabel first?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-review-queue?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a local text dataset labelled with task types (research, writing, analysis, implementation) and need to identify which predictions from a naive-Bayes model are uncertain enough to warrant human review. It is ideal for data quality audits, pre-retraining cleaning, and lightweight evaluation workflows where no hosted model or foundation-model training is needed.
Side effects
- Fewer than 1 or more than 200 records supplied — request rejected
- Records missing required fields (id, text, actual, predicted, probabilities) — validation error
- Probabilities array does not contain exactly 4 values summing approximately to 1 — schema rejection
- Invalid enum values for actual or predicted fields — rejected
- Empty records array returns no meaningful review queue
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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