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Language Routing – Low-Confidence Review Queue
x402
Language Routing – 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 language classifier (English/French/Spanish/other) for human review, given up to 200 labelled text records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Language routing - Low-confidence review queue: Low-confidence review queue for Language routing. 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 records where the naive-Bayes language classifier's predictions are low-confidence, drawn from up to 200 supplied labelled text records. Each queued item includes its ID, text, actual label, predicted label, and probability scores, helping users identify which classifications need human review or further investigation.
Example use cases
Auditing a multilingual customer support classifier
“I've got 120 customer support tickets labelled as english, french, spanish, or other, along with my model's predicted labels and probability scores — can you generate a low-confidence review queue so I know which ones the language router was unsure about and need a human to double-check?”
QA pass before production language routing deployment
“Before I push my language routing model to production, I want to flag the uncertain cases — here are 80 text samples with their actual and predicted language labels and their four-class probabilities. Can you run the low-confidence review queue and tell me which records fall below a reliable confidence threshold?”
Evaluating naive-Bayes baseline on a mixed-language dataset
“I'm benchmarking a naive-Bayes language classifier on 200 labelled records spanning english, french, spanish, and other. Can you surface the low-confidence predictions so I can see where the baseline struggles most and decide which samples to prioritise for relabelling?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-language-routing-review-queue?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a local language classification pipeline (English/French/Spanish/other) using a naive-Bayes or similar baseline model and want to systematically identify which predictions are uncertain enough to warrant human review. It is well-suited for small-to-medium batch evaluation (up to 200 records) without needing a hosted model or foundation-model infrastructure. Prefer it over general ML evaluation tools when your specific use case is language routing quality assurance and you need a structured low-confidence queue deliverable.
Side effects
- More than 200 records supplied — input truncated or rejected
- Probability arrays not summing to 1 or not containing exactly 4 values — validation error
- Missing required fields (id, text, actual, predicted, probabilities) in any record — schema rejection
- Enum values outside allowed set (english/french/spanish/other) — validation failure
- Payment not completed via x402 — endpoint returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| minimum_confidence | number |
Tags
x402
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