Answers and explanations · Check the judge as well as the answer

1. The clarification can pass all three criteria: it asks for missing context, claims no unsupported fact, promises no staff-only action, and discloses no contact information. This judgment depends on the supplied fictional context. An actual unsupported claim would fail grounded; the absence of such a claim is not itself a hallucination.
2. Positive means acceptable. TP=2, FP=1, FN=1, TN=2. Acceptance precision=2/3; acceptance recall=2/3; false-acceptance rate=1/3. The final denominator contains all reference-unacceptable examples, including those correctly rejected. Dividing FP by all six examples would answer a different question.
3. false_acceptance_rate is None/JSON null, because FP+TN=0. Precision and recall are both 1 for the one acceptable example. Add independently reviewed unacceptable and borderline cases before making a claim about false acceptances; one accepted example cannot establish safety.

import opslab
print(opslab.grader_calibration([
    {"id": "single", "reference_ok": True, "judge_ok": True}
]))

4. Route and response content are different objects of evaluation. s2 can have the expected route while its text asserts an unapproved refund. Example recommendation: “Hold semantic approval for s2 and s3; the route-only result does not cover their content. Adjudicate s2's grounded criterion using the every-assertion anchor and retain both original ratings, even though overall reviewer agreement is 11/12.”

Self-check: a complete answer names the positive class, preserves undefined denominators, and treats agreement as consistency rather than correctness. The supplied rubric aggregation is runnable; no automatic semantic model has been implemented or benchmarked.