{"id":"W4234247114","doi":"10.2139/ssrn.3338718","title":"Sense and Similarity: Automating Legal Text Comparison","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sense (electronics); Similarity (geometry); Computer science; Natural language processing; Information retrieval; Artificial intelligence; Engineering","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":[0.003193864,0.001378993,0.002123802,0.01771711,0.001408067,0.003974614,0.002070838,0.002081016,0.01544811],"category_scores_gemma":[0.02859944,0.0007769565,0.001527525,0.007511111,0.0007629541,0.009284859,0.005484508,0.001216774,0.009763091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007244149,"about_ca_system_score_gemma":0.001452267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002664992,"about_ca_topic_score_gemma":0.004845766,"domain_scores_codex":[0.9952552,0.0009458309,0.0005989973,0.001378202,0.001623134,0.0001985464],"domain_scores_gemma":[0.9836037,0.01044201,0.0009973476,0.001860767,0.002491283,0.0006048959],"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":[0.0008773129,0.0002516947,0.005419503,0.0006411254,0.000207938,0.0003236216,0.0007283261,0.001639921,0.02019158,0.005976381,0.03235373,0.9313889],"study_design_scores_gemma":[0.0003774682,0.0007870539,0.02084514,0.0003174496,0.0005711492,0.002558276,0.003622627,0.6839893,0.07752739,0.122731,0.08643931,0.0002338603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1131376,0.00283787,0.775524,0.001192613,0.0009268302,0.001106478,0.01035013,0.08208492,0.01283948],"genre_scores_gemma":[0.2710327,0.0006781127,0.7044445,0.0003857807,0.0004397826,0.0004966551,0.0134458,0.002786651,0.006289945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01771711,"threshold_uncertainty_score":0.05167913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921546106449809,"score_gpt":0.334922178260661,"score_spread":0.3157067171961629,"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."}}