{"id":"W4382318552","doi":"10.1609/aaai.v37i11.26610","title":"Identify Event Causality with Knowledge and Analogy","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institute for Advanced Research","keywords":"Analogy; Causality (physics); Computer science; Generalizability theory; Event (particle physics); Identification (biology); Artificial intelligence; Benchmark (surveying); Natural language processing; Sample (material); Machine learning; Data science; Epistemology; Mathematics; Statistics","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":[0.0006193005,0.0001593298,0.0002035833,0.0001408707,0.0001613814,0.0001669507,0.001168485,0.00006070704,0.00001367007],"category_scores_gemma":[0.0001578739,0.0001109302,0.00004731975,0.0008370549,0.0002063604,0.0002880353,0.0005472014,0.0002040461,0.00007917902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002694693,"about_ca_system_score_gemma":0.00008480274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000543269,"about_ca_topic_score_gemma":0.00004250254,"domain_scores_codex":[0.998518,0.00001825277,0.0003572963,0.000488574,0.0003170293,0.0003008218],"domain_scores_gemma":[0.9989666,0.00007976282,0.000187633,0.0002913904,0.0003921809,0.00008248349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001816621,0.00005263144,0.001749978,0.00005403906,0.0000144418,0.000001099735,0.002090954,0.00008670206,0.01008487,0.9273486,0.0001184323,0.05838006],"study_design_scores_gemma":[0.00006021018,0.0002919577,0.008671747,0.0003834568,0.00002286609,0.00001831975,0.0009720626,0.3614955,0.2378479,0.3895849,0.0002366533,0.000414401],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9118541,0.00004102102,0.07609181,0.005018238,0.0004370839,0.0003918079,0.000002817836,0.0002277757,0.005935389],"genre_scores_gemma":[0.9982683,0.00003508222,0.001229491,0.00006738873,0.00004202469,0.00001850816,2.34389e-7,0.000007843351,0.0003311149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5377637,"threshold_uncertainty_score":0.4523603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.108560566897802,"score_gpt":0.3440230648690443,"score_spread":0.2354624979712422,"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."}}