{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007593483,0.0001998283,0.0002426292,0.000157443,0.0003634515,0.0002986281,0.001139002,0.0001784866,0.007586548],"category_scores_gemma":[0.003661244,0.0002194593,0.0000929633,0.0004054464,0.0004273185,0.0006541152,0.0003460924,0.0003789177,0.0001913081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001771627,"about_ca_system_score_gemma":0.0006403852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007395303,"about_ca_topic_score_gemma":0.004058699,"domain_scores_codex":[0.9971377,0.0002827942,0.0004561536,0.0006812313,0.00110816,0.0003339254],"domain_scores_gemma":[0.9975883,0.0007569057,0.0002383167,0.0006027681,0.0006820873,0.0001316623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007980391,0.0008303061,0.002242404,0.00002118285,0.00033366,0.0005405995,0.00909053,0.0001706641,0.01396362,0.3688583,0.01550308,0.5876476],"study_design_scores_gemma":[0.000811457,0.001120454,0.002530669,0.002622366,0.000217192,0.00008758644,0.1182763,0.06858777,0.1207059,0.004882797,0.677784,0.002373446],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7660227,0.0001595328,0.003504929,0.0167831,0.008053172,0.0004106943,0.001772118,0.0001569017,0.2031369],"genre_scores_gemma":[0.9932322,0.000213545,0.0007103154,0.0004239958,0.0008651325,0.00001303197,0.0003508883,0.00001885844,0.004172039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6622809,"threshold_uncertainty_score":0.9933206,"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."}}