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Project Status Low-Confidence Review Queue
x402
Project Status 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 2026-10-19).
Identifies and queues low-confidence predictions from a naive-Bayes project status classifier for human review, returning a bounded deliverable for up to 200 labelled text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project status - Low-confidence review queue: Low-confidence review queue for Project status. 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 identifying low-confidence records from the submitted batch — typically a structured list of flagged record IDs and associated metadata (predicted label, actual label, probabilities) where the classifier's confidence fell below an acceptable threshold, suitable for routing to a human reviewer.
Example use cases
Auditing uncertain sprint status labels
“I've got 80 labelled sprint records where my classifier predicted planned, active, blocked, or complete — can you run them through the low-confidence review queue so I know which predictions I should have a human verify before finalizing the report?”
Pre-release model validation for project tracker
“Before we ship our project status classifier, I want to flag any low-confidence predictions in our 150-record test set. Each record has the text, actual status, predicted status, and four probabilities. Can you generate the review queue so we know what to manually inspect?”
Ongoing QA for automated project status pipeline
“Our automated pipeline labelled 120 project update texts as planned, active, blocked, or complete — can you run the low-confidence review queue on those records with their prediction probabilities so we can catch any uncertain calls before they go into the dashboard?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-project-status-review-queue?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have an existing set of project status predictions from a local naive-Bayes or similar classifier and need to programmatically identify which predictions are uncertain enough to warrant human review. It is purpose-built for the planned/active/blocked/complete label space and works with small local datasets without requiring a hosted model or foundation model. Prefer this over general-purpose ML evaluation tools when you want a lightweight, pay-per-call review queue focused specifically on project status classification confidence.
Side effects
- Batch exceeds 200 records — request rejected or truncated
- Malformed probability arrays (not exactly 4 values summing to ~1) — validation error
- Invalid enum values for actual or predicted fields — schema rejection
- Empty records array — no review queue generated
- Payment not completed via x402 protocol — request not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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