{"id":"W4319659350","doi":"10.1007/s10506-023-09346-x","title":"Correction: Using attention methods to predict judicial outcomes","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence and Law","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Philosophy of law; Computer science; Legal aspects of computing; Artificial intelligence; Cognitive psychology; Psychology; Political science; Law; The Internet; Comparative law; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.001789141,0.00008462102,0.0001613723,0.0001479274,0.0008688497,0.0001287118,0.0001052485,0.00005972023,0.0001673189],"category_scores_gemma":[0.00042869,0.00008085361,0.00008786761,0.00108667,0.0001923918,0.0001579731,0.00005642002,0.0000786519,0.000152813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003444964,"about_ca_system_score_gemma":0.00004764966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005719305,"about_ca_topic_score_gemma":0.004609346,"domain_scores_codex":[0.9986407,0.0003562919,0.000262762,0.0002398916,0.0002636114,0.000236739],"domain_scores_gemma":[0.9990451,0.0005817831,0.00005079202,0.00008145258,0.0001149362,0.0001259544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001455568,0.00003136743,0.002971101,0.0000029078,0.00003660146,0.000003689154,0.003911223,0.002627455,0.0007067805,0.4350428,0.0004073928,0.5542442],"study_design_scores_gemma":[0.00004520413,0.0001453048,0.0193273,0.00006594549,0.0001735022,0.000003446744,0.02069762,0.1829375,0.00424645,0.6906331,0.0810736,0.0006510242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1651988,0.00004098542,0.8104962,0.004432467,0.004065492,0.0002874026,0.000003689652,0.0002942659,0.01518073],"genre_scores_gemma":[0.9784701,0.00002287438,0.01874143,0.000529199,0.0006142633,0.00001135717,0.000005589134,0.000009179835,0.001595988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8132713,"threshold_uncertainty_score":0.8645916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1992209616846759,"score_gpt":0.4873160492234294,"score_spread":0.2880950875387535,"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."}}