{"uid":"cap_S2vRS4vvLp7k-hZbaeTR6","slug":"training-question-low-confidence-review-queue-41a0809c","name":"Training Question Low-Confidence Review Queue","description":"Training question - Low-confidence review queue: Low-confidence review queue for Training question. 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.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-review-queue?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted","probabilities"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["concept","procedure","example","assessment"],"type":"string"},"predicted":{"enum":["concept","procedure","example","assessment"],"type":"string"},"probabilities":{"type":"array","items":{"type":"number","default":0,"maximum":1,"minimum":0},"default":[],"maxItems":4,"minItems":4}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"concept sample document number 0 for this 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dataset.","actual":"procedure","predicted":"procedure","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"procedure sample document number 2 for this supplied dataset.","actual":"procedure","predicted":"procedure","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"example sample document number 0 for this supplied dataset.","actual":"example","predicted":"example","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"example sample document number 1 for this supplied dataset.","actual":"example","predicted":"example","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"example sample document number 2 for this supplied dataset.","actual":"example","predicted":"example","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"assessment sample document number 0 for this supplied dataset.","actual":"assessment","predicted":"assessment","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"assessment sample document number 1 for this supplied dataset.","actual":"assessment","predicted":"assessment","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"assessment sample document number 2 for this supplied 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low-confidence predictions in a labeled training dataset of educational text records classified as concept, procedure, example, or assessment, returning a review queue for human inspection.","exampleAgentPrompt":"I have up to 200 labeled training records for my question classifier — each with an ID, text, actual label (concept, procedure, example, or assessment), predicted label, and four class probabilities — can you run them through the low-confidence review queue so I can see which ones my model is most uncertain about and need human review?","exampleUseCases":[{"title":"Pre-retraining data audit","prompt":"Before I retrain my question classifier, run these 150 labeled records through the low-confidence review queue — each one has an ID, the text, the actual label, predicted label, and four class probabilities — and show me which ones scored low confidence so I can fix the labels first."},{"title":"Catching mislabeled procedure examples","prompt":"I've got 80 training samples tagged as either concept, procedure, example, or assessment with their predicted labels and probability scores — can you pull the low-confidence review queue so I can see which procedure and concept records my model is mixing up?"},{"title":"Quality gate for new annotation batch","prompt":"My team just finished labeling 200 new training questions as concept, procedure, example, or assessment — run the low-confidence review queue on them with their predicted labels and probabilities so I can flag anything uncertain before adding it to the training set."}],"resultDescription":"A bounded deliverable containing a review queue of up to 200 text records identified as low-confidence by a naive-Bayes baseline, including the flagged record IDs, texts, actual and predicted labels, and probability distributions that triggered the low-confidence threshold — intended for human review and relabeling.","failureModes":["Records array exceeds 200 items — request rejected or truncated","Probabilities array does not contain exactly 4 values — validation error","Probability values outside [0,1] range — schema validation failure","Invalid label values outside the enum (concept, procedure, example, assessment) — rejected","Empty records array returns empty review queue","Payment not processed (x402 protocol failure) — endpoint not served"],"whenToPreferThis":"Choose this endpoint when you have a small labeled text dataset (up to 200 records) classified into the four educational categories (concept, procedure, example, assessment) and need to identify which records a naive-Bayes model is least confident about for human review — particularly useful before retraining or during annotation quality control. Not suitable for large-scale datasets, foundation model training, or classification domains outside educational question types.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T00:49:27.822Z","isFirstParty":false,"canonicalSlug":"training-question-low-confidence-review-queue-41a0809c"}