{"id":"W2785356083","doi":"10.1609/aaai.v32i1.11561","title":"Situation Calculus Semantics for Actual Causality","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semantics (computer science); Calculus (dental); Causality (physics); Computer science; Programming language; Mathematics; Algebra over a field; Theoretical computer science; Medicine; Pure mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007153665,0.0002221378,0.0002465417,0.00009233095,0.0003315213,0.0002855319,0.001780821,0.0001250403,0.0000265136],"category_scores_gemma":[0.0006074856,0.0001692669,0.0001245932,0.0005037732,0.0003915735,0.0004128852,0.0002925973,0.0002051838,0.00008349698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004596649,"about_ca_system_score_gemma":0.0001551043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004471573,"about_ca_topic_score_gemma":0.0000255667,"domain_scores_codex":[0.9980768,0.00001731277,0.000527994,0.0005352496,0.0004472829,0.0003953904],"domain_scores_gemma":[0.9974518,0.0001190971,0.000343831,0.0003656289,0.001617975,0.0001016422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004554988,0.00008982352,0.00003547392,0.00002805201,0.00001154346,8.459091e-8,0.001512455,0.00001608543,0.02870974,0.9186717,0.0002966821,0.05058277],"study_design_scores_gemma":[0.00002459712,0.0002957463,0.00006886014,0.00009181435,0.00001242429,0.000002229583,0.0001606633,0.2937982,0.4463622,0.2588991,0.0001081436,0.0001760384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08799759,0.000007005984,0.9024037,0.004838376,0.0006645233,0.0004530165,0.000008739039,0.0001266388,0.003500454],"genre_scores_gemma":[0.9867323,0.000009409728,0.01245222,0.0003456612,0.0001983167,0.00003163537,7.630711e-7,0.00001161468,0.0002180834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8987347,"threshold_uncertainty_score":0.6902504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1143655799003566,"score_gpt":0.3318612548278091,"score_spread":0.2174956749274524,"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."}}