{"id":"W3184429340","doi":"","title":"Common-Sense Causation: How a Robust and Pragmatic Application of the 'But For' Test Can Solve the Circular Causation Problem in Cases of Multiple Contributing Tortfeasors","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Legal principles and applications","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Causation; Tort; Test (biology); Liability; Plaintiff; Compensation (psychology); Computer science; Law and economics; Psychology; Law; Political science; Economics; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06113943,0.001163756,0.00187556,0.003412717,0.007458108,0.01096552,0.005548677,0.01300991,0.00720271],"category_scores_gemma":[0.1611626,0.0008843084,0.002154059,0.002071545,0.04306481,0.01755437,0.01000102,0.009511294,0.0009018768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008214346,"about_ca_system_score_gemma":0.01465317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03288857,"about_ca_topic_score_gemma":0.03031848,"domain_scores_codex":[0.9602358,0.02438357,0.002148986,0.004810553,0.005775896,0.002645046],"domain_scores_gemma":[0.9027534,0.07721348,0.004653781,0.008170487,0.00599093,0.001218031],"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":[0.00001853122,0.0000220989,0.0008638696,0.00004749306,0.00003053452,0.0004539229,0.002330246,0.001183009,0.0001601389,0.9801267,0.003340721,0.01142271],"study_design_scores_gemma":[0.00004214501,0.0000376017,0.0006636265,0.0001029152,0.0000504228,0.0002140519,0.001306988,0.007760127,0.0004487535,0.9747952,0.01450522,0.00007296603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0495945,0.001255314,0.5884895,0.158719,0.001632417,0.0009341597,0.000189269,0.0008876646,0.1982981],"genre_scores_gemma":[0.7974599,0.0003001395,0.1786878,0.01170157,0.0003622422,0.0004603713,0.00006861144,0.0001991801,0.01076029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06113943,"threshold_uncertainty_score":0.3233401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664094445243055,"score_gpt":0.2708454016434107,"score_spread":0.2542044571909802,"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."}}