{"id":"W4297877413","doi":"10.3390/informatics9040072","title":"Semantic Annotation of Legal Contracts with ContrattoA","year":2022,"lang":"en","type":"article","venue":"Informatics","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Natural language processing; Ontology; Annotation; Quality (philosophy); Set (abstract data type); Artificial intelligence; Sample (material); Process (computing); Obligation; Information retrieval; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005903366,0.00005276371,0.0001105176,0.00004972982,0.0004466398,0.0000445208,0.0001845834,0.00002325065,0.0002828785],"category_scores_gemma":[0.0001322462,0.00004941989,0.00002461229,0.000258055,0.0002623674,0.0004947931,0.0000273867,0.0001183262,0.00002193107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006448522,"about_ca_system_score_gemma":0.000236569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001219276,"about_ca_topic_score_gemma":0.0009959519,"domain_scores_codex":[0.998854,0.0000645932,0.0003451041,0.00003596789,0.0005181437,0.0001821501],"domain_scores_gemma":[0.999309,0.0001567533,0.0002406606,0.0001027022,0.0001439013,0.00004699851],"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":[0.0001060975,0.0001291365,0.002945655,0.00004041625,0.00004336437,0.00001057797,0.1742075,0.01411372,0.0001771731,0.7929428,0.00191256,0.01337103],"study_design_scores_gemma":[0.0005356773,0.0007060464,0.0009129553,0.00005051398,0.00006917972,0.00001944704,0.3551234,0.01949502,0.00486934,0.006379371,0.6113498,0.0004893476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8193024,0.00002111959,0.005994833,0.0007884357,0.0003531053,0.0004439004,0.00002132722,0.00007142223,0.1730035],"genre_scores_gemma":[0.9985412,0.000004469427,0.0007574047,0.0002899083,0.00003581621,0.00001384333,0.00000491549,0.000004263128,0.0003481563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7865634,"threshold_uncertainty_score":0.3435237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226633246408767,"score_gpt":0.3061803025750932,"score_spread":0.2839139701110055,"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."}}