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Knowledge Topic Low-Confidence Review Queue
x402
Knowledge Topic 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 2026-10-15).
Identifies and queues low-confidence predictions from a naive-Bayes knowledge topic classifier (setup/usage/troubleshooting/reference) for human review, returning evaluation metrics and borderline cases.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Low-confidence review queue: Low-confidence review queue for Knowledge topic. 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
Returns a bounded deliverable containing a low-confidence review queue: a filtered list of records where the classifier's predicted probabilities indicate uncertainty, along with evaluation metrics (accuracy, per-class performance) computed against actual labels and naive-Bayes baseline comparisons. Highlights records most in need of human review.
Example use cases
Documentation classifier uncertainty audit
“I've got 150 support docs that my classifier labelled as setup, usage, troubleshooting, or reference, and I have the actual labels plus the 4-class probabilities for each. Which ones did the model predict with low confidence and should be flagged for a human reviewer?”
Naive-Bayes baseline evaluation on knowledge topics
“Can you evaluate my naive-Bayes knowledge topic model? I'll give you the predicted labels, actual labels, and probability arrays for each of my 80 labelled records, and I want to see which ones fall into the low-confidence review queue.”
Identifying mislabelled or ambiguous knowledge articles
“We're building a help centre and our auto-classifier struggles on some articles — I have 200 text records with their setup/usage/troubleshooting/reference predictions and true labels along with class probabilities. Please generate a review queue of the low-confidence and likely wrong ones.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-review-queue?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a local knowledge topic classifier producing 4-class probability outputs (setup/usage/troubleshooting/reference) and want to identify which labelled records fall below confidence thresholds for human review. Ideal for small-dataset evaluation (up to 200 records) without access to a hosted model or foundation-model training pipeline. Prefer this over general ML evaluation tools when the specific topic taxonomy (setup/usage/troubleshooting/reference) matches your use case and you want a structured review queue output rather than raw metrics.
Side effects
- Records array exceeds 200 items — request rejected
- Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error
- Probabilities array does not contain exactly 4 values or values outside [0,1] — schema validation failure
- Enum values for actual/predicted outside allowed set (setup, usage, troubleshooting, reference) — rejected
- Empty records array — no queue generated
- Network timeout or payment failure via x402 protocol
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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