{"id":"W4205504967","doi":"10.1109/icdmw53433.2021.00084","title":"Detection of Similar Legal Cases on Personal Injury","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Data Mining Workshops (ICDMW)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Personal injury; Computer science; Plaintiff; Compensation (psychology); Similarity (geometry); Artificial intelligence; Deep learning; Variation (astronomy); Legal research; Feature (linguistics); Style (visual arts); Data science; Natural language processing; Information retrieval; Psychology; Law; Social psychology; Political science; Linguistics","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":[0.002409946,0.0004552486,0.0004757862,0.007463151,0.001215568,0.001336255,0.00145959,0.001924552,0.001522312],"category_scores_gemma":[0.01349123,0.0002048076,0.0006790577,0.002009531,0.001074162,0.00145644,0.001519593,0.0009349145,0.0005689394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001744352,"about_ca_system_score_gemma":0.001682207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02074328,"about_ca_topic_score_gemma":0.03784257,"domain_scores_codex":[0.9972755,0.0004821384,0.0002784,0.0006624139,0.0009803615,0.00032122],"domain_scores_gemma":[0.9932106,0.003403686,0.001057595,0.0006134824,0.001353658,0.0003610443],"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":[0.001047805,0.001443309,0.3432822,0.0008284376,0.0002587012,0.01424071,0.003801829,0.0692035,0.0207869,0.01040598,0.03260595,0.5020947],"study_design_scores_gemma":[0.00006362109,0.0002222175,0.1017628,0.0002036444,0.0001089016,0.003509758,0.003261193,0.8395864,0.0246382,0.01162048,0.01495325,0.00006955922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9375702,0.0008676875,0.05029146,0.001374438,0.0001064132,0.0005803439,0.002542465,0.001041141,0.005625943],"genre_scores_gemma":[0.9542871,0.0002141003,0.03880923,0.0002031727,0.0000464162,0.00009328869,0.004386936,0.0000266664,0.001933065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02074328,"threshold_uncertainty_score":0.0412451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2099168421139607,"score_gpt":0.4157965502019689,"score_spread":0.2058797080880082,"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."}}