{"id":"W1996571324","doi":"10.1139/l06-122","title":"An integrated methodology for collecting, classifying, and analyzing Canadian construction court cases","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Arbitration; Dispute resolution; Negotiation; Construction contract; Process (computing); Categorization; Computer science; Law; Operations research; Artificial intelligence; Engineering; Political science; Business; Contract management","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003082989,0.0001115086,0.0002226131,0.000828171,0.0006146208,0.0001324919,0.0001794768,0.0001607288,0.0001475751],"category_scores_gemma":[0.00315533,0.0001247183,0.00005761543,0.0004385693,0.0002477048,0.0002926558,0.00000237278,0.0002620268,6.043115e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007694635,"about_ca_system_score_gemma":0.0030271,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5544181,"about_ca_topic_score_gemma":0.998994,"domain_scores_codex":[0.9987058,0.00008806448,0.0003804336,0.0001237567,0.0001079425,0.0005940182],"domain_scores_gemma":[0.9972872,0.0006669575,0.0001527727,0.00007349121,0.0004284335,0.00139121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002260073,0.00004684551,0.2497355,0.0001942823,0.0008408331,0.00303227,0.1059278,0.07236727,0.008512673,0.3190561,0.01258732,0.2274731],"study_design_scores_gemma":[0.0004758048,0.0006639556,0.005048423,0.000395884,0.0002184917,0.001494422,0.06949297,0.0120507,0.00342596,0.00371615,0.9019541,0.00106312],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3928243,0.001065391,0.5949267,0.00123664,0.004372681,0.0004182366,0.0000400697,0.00005186324,0.005064102],"genre_scores_gemma":[0.982749,0.00001995255,0.01663752,0.00006087065,0.0004691971,0.000001437582,0.000001515301,0.00001825479,0.00004227395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8893668,"threshold_uncertainty_score":0.5369945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09898733384824149,"score_gpt":0.3582047168931432,"score_spread":0.2592173830449017,"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."}}