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Content Purpose Classifier – Low-Confidence Review Queue
x402
Content Purpose Classifier – 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 content-purpose classifier and returns a structured review queue of up to 200 labelled text records for human inspection.
Price / call$0.01
Success—
StatusUnknown
How this service works
Content purpose - Low-confidence review queue: Low-confidence review queue for Content purpose. 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 listing the subset of submitted records where the classifier's predicted content-purpose label (inform, explain, persuade, instruct) fell below confidence thresholds, enabling targeted human review and relabelling. Includes flagged record IDs, texts, actual vs predicted labels, and associated probabilities.
Example use cases
QA pipeline for content classifier
“We just ran our naive-Bayes content-purpose model over 150 articles and got back predicted labels and probabilities for each — can you push them all through the low-confidence review queue so I know which ones a human editor should double-check before we publish?”
Auditing persuade vs instruct confusion
“My classifier keeps mixing up 'persuade' and 'instruct' labels on some documents. I have the record IDs, texts, actual labels, predicted labels, and probabilities for about 80 records — run them through the low-confidence review queue and tell me which ones scored below threshold.”
Pre-launch model validation
“Before we ship this content-purpose classifier, I want to surface any borderline cases. I'll give you the full set of 200 labelled examples with their probability outputs — queue up all the low-confidence ones so the team can review and relabel them if needed.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-content-purpose-review-queue?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a local content-purpose text classification dataset with predicted labels and class probabilities and want to automatically surface the low-confidence cases for human review. It is ideal for human-in-the-loop QA workflows, pre-deployment audits, or iterative relabelling pipelines where you need a bounded, cost-effective review queue without hosting a model or calling a foundation model API.
Side effects
- More than 200 records submitted — input exceeds the maximum batch size
- Probabilities array does not contain exactly 4 values per record — schema validation error
- Probabilities values outside [0,1] range — rejected at input validation
- Missing required fields (id, text, actual, predicted, probabilities) — schema validation failure
- Label values outside allowed enum (inform, explain, persuade, instruct) — rejection
- Empty records array — no output produced
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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