Output Interpretation
AvelinLabs outputs are designed as decision-support signals for applications, dashboards, and workflows.
For a practical evaluation flow that compares strong and weak job descriptions, see Evaluate AvelinLabs in your workflow.
For concrete input-quality comparisons across strong, title-only, vague, ambiguous, and noisy text, see Input quality examples.
For practical field-by-field usage guidance, see the Response Field Reference.
For measurable coverage, review-routing, confidence, uncertainty, weak-signal, and evidence metrics, see Metrics you can derive from AvelinLabs responses.
Complete executable examples belong in the AvelinLabs API examples repository.
Shared Decision Fields
Section titled “Shared Decision Fields”confidence
Section titled “confidence”Meaning: Overall confidence signal. It helps rank or route results but should not be treated as absolute truth.
confidence_level
Section titled “confidence_level”Meaning: Human-readable confidence category intended for display and review workflows.
trust_score
Section titled “trust_score”Meaning: Trust-oriented score combining result strength and quality support.
uncertainty.aleatoric
Section titled “uncertainty.aleatoric”Meaning: Signal for uncertainty associated with noisy or ambiguous input evidence.
uncertainty.epistemic
Section titled “uncertainty.epistemic”Meaning: Signal for uncertainty associated with limited support in available evidence.
uncertainty.total
Section titled “uncertainty.total”Meaning: Combined uncertainty signal.
decision.decision
Section titled “decision.decision”Meaning: Routing label such as AUTO_ACCEPT, REVIEW, REJECT, or AMBIGUOUS.
decision.decision_score
Section titled “decision.decision_score”Meaning: Numeric signal supporting the routing label.
decision.reason
Section titled “decision.reason”Meaning: Human-readable explanation for the routing label.
decision.applied_rule
Section titled “decision.applied_rule”Meaning: Rule identifier for the routing decision. Treat as metadata that may evolve.
is_ambiguous
Section titled “is_ambiguous”Meaning: Indicates that the output should be treated carefully or routed to review.
domain_is_ambiguous
Section titled “domain_is_ambiguous”Meaning: Indicates ambiguity about whether the input fits the supported domain.
How to Interpret AvelinLabs Outputs
Section titled “How to Interpret AvelinLabs Outputs”- Scores are decision-support signals, not absolute truth.
- Confidence indicates how reliable or complete the available evidence appears to be for the requested task.
- Occupation matches are structured suggestions grounded in occupation intelligence.
- Skill signals may come from explicit text or inferred context, depending on endpoint behavior and available input.
- Recommendations should be reviewed in the context of the user’s workflow, risk tolerance, and automation policy.
- Ambiguous, weak-signal, or low-confidence outputs should generally be reviewed before being used for automation.
- Weak, vague, repeated-keyword, or non-occupational inputs may still receive a best-effort match, but public confidence is intentionally damped when occupational evidence is limited.
Response Stability During Beta
Section titled “Response Stability During Beta”AvelinLabs is currently in beta / early access.
Core public response concepts are intended to remain stable: job inputs, occupation outputs, ranked matches, confidence, uncertainty, skill evidence, explanations, and market context.
During beta:
- additive fields may be introduced
- optional fields may be absent for some requests
- clients should ignore unknown fields
- clients should not depend on debug-only fields
- examples are illustrative and may evolve with the beta