{"id":"W4387846619","doi":"10.1145/3583780.3615308","title":"The 3rd International Workshop on Mining and Learning in the Legal Domain","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada)","funders":"European Commission","keywords":"Computer science; Domain (mathematical analysis); Variety (cybernetics); Data science; Generative grammar; Legal research; Domain knowledge; Knowledge management; Artificial intelligence; Political science; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0213849,0.001550947,0.002324102,0.004401567,0.0015283,0.01096321,0.004425623,0.003580508,0.02106348],"category_scores_gemma":[0.03374697,0.001051949,0.00465043,0.003549353,0.002722514,0.009370667,0.006704003,0.008507996,0.007191476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003233456,"about_ca_system_score_gemma":0.006470619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006132498,"about_ca_topic_score_gemma":0.008010289,"domain_scores_codex":[0.9910148,0.004132939,0.0008093413,0.001745021,0.0016491,0.0006488305],"domain_scores_gemma":[0.9713829,0.01678878,0.0005793174,0.003719958,0.004589314,0.002939714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003981661,0.0006322337,0.001673964,0.0009439005,0.000294555,0.000490943,0.001614656,0.006758899,0.001715065,0.04277319,0.4820777,0.4606265],"study_design_scores_gemma":[0.00009442629,0.0002165605,0.002343958,0.001016458,0.0001140568,0.0005727667,0.001256182,0.0294884,0.002229686,0.09834095,0.864202,0.0001245974],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01476913,0.06463793,0.7048994,0.09624492,0.05322329,0.001320894,0.006412223,0.003831024,0.05466107],"genre_scores_gemma":[0.07740865,0.03568715,0.6017674,0.01379546,0.02400823,0.001870553,0.02492212,0.002656287,0.2178842],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0213849,"threshold_uncertainty_score":0.1130955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06980716919431978,"score_gpt":0.3886975378999868,"score_spread":0.318890368705667,"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."}}