{"id":"W1514731943","doi":"10.1007/978-3-540-74407-8_33","title":"Temporal Antecedent Failure: Refining Vacuity","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Antecedent (behavioral psychology); Computer science; Temporal logic; Model checking; Extension (predicate logic); Linear temporal logic; Theoretical computer science; Programming language; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003652324,0.0006140123,0.0005813973,0.001188414,0.0003470484,0.0006261314,0.004965726,0.0005162034,0.00002473449],"category_scores_gemma":[0.0002572997,0.0005814588,0.0001615624,0.00103261,0.0007521412,0.001052662,0.001826533,0.001353607,0.0000982628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006080968,"about_ca_system_score_gemma":0.0005211685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003736915,"about_ca_topic_score_gemma":0.0001385515,"domain_scores_codex":[0.994808,0.00006607265,0.0008224377,0.001857436,0.001532,0.0009140831],"domain_scores_gemma":[0.9963368,0.0003658053,0.0005345657,0.002197948,0.0003249172,0.0002400166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006452093,0.00002390483,0.000163799,0.00003528537,0.000007900048,0.00008912935,0.0005016374,0.002562361,0.0001795013,0.1014986,0.00002631478,0.8949051],"study_design_scores_gemma":[0.0006607979,0.0005043349,0.001128768,0.001052512,0.00001937992,0.0004773697,6.667613e-7,0.7374093,0.009817364,0.2155531,0.03074733,0.00262906],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005883191,0.0002329264,0.9877418,0.0003780064,0.002770093,0.000252821,0.000003197185,0.0003251358,0.008237234],"genre_scores_gemma":[0.02339561,0.00001739681,0.9746954,0.0009996662,0.0004594766,0.000006787907,0.000005103628,0.00003707494,0.0003834566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8922761,"threshold_uncertainty_score":0.9996637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05001429136310787,"score_gpt":0.3104683291280238,"score_spread":0.2604540377649159,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}