{"id":"W4387124147","doi":"10.1109/re57278.2023.00042","title":"Towards Legal Contract Formalization with Controlled Natural Language Templates","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Template; Natural language; Framing (construction); Programming language; Design by contract; Context (archaeology); Software engineering; Natural language processing; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006307101,0.00009179045,0.0001787274,0.00007348239,0.0004279006,0.0001738329,0.0001750084,0.0000670099,0.0007807281],"category_scores_gemma":[0.0003895304,0.00006324285,0.00005564792,0.0004948073,0.0002151141,0.0005540849,0.00001974576,0.00009194374,0.0003474582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005053532,"about_ca_system_score_gemma":0.0001429952,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009948961,"about_ca_topic_score_gemma":0.02171871,"domain_scores_codex":[0.9988239,0.00008787201,0.0001917295,0.0001470429,0.000394726,0.0003547193],"domain_scores_gemma":[0.9994153,0.0002114349,0.00006567334,0.00009880513,0.000133985,0.00007481864],"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":[0.0005420871,0.00004936898,0.002682031,0.000007929892,0.0000841514,0.00008841524,0.02738922,0.0003108961,0.001261582,0.9374377,0.005119685,0.02502696],"study_design_scores_gemma":[0.009396728,0.0009469462,0.01082233,0.0002073927,0.0002887035,0.00003106581,0.3543254,0.1313126,0.06203332,0.01438814,0.4132305,0.003016832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5930464,0.0001124981,0.002014771,0.005045878,0.0007558288,0.0009476925,0.000005033889,0.001101587,0.3969703],"genre_scores_gemma":[0.9870807,0.00001685876,0.0001626459,0.0003434325,0.0002168842,0.0000281556,0.00001374582,0.00001062234,0.01212694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9230495,"threshold_uncertainty_score":0.9966439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343172291129712,"score_gpt":0.3533764153706355,"score_spread":0.3299446924593383,"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."}}