{"id":"W4404781360","doi":"10.18653/v1/2024.nllp-1.4","title":"uOttawa at LegalLens-2024: Transformer-based Classification Experiments","year":2024,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Transformer; Engineering; Electrical engineering; Voltage","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004615637,0.0001088243,0.00009526568,0.00008222834,0.0005021168,0.0002052413,0.0002278426,0.00009635054,0.01144086],"category_scores_gemma":[0.00004548512,0.00009858982,0.000104829,0.0003420762,0.0003592476,0.0003610257,0.000008885209,0.0001052864,0.002856514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003738048,"about_ca_system_score_gemma":0.0002348849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001652002,"about_ca_topic_score_gemma":0.005770515,"domain_scores_codex":[0.998557,0.00009630854,0.0002425865,0.0003140373,0.000457301,0.0003327348],"domain_scores_gemma":[0.9994814,0.0001474582,0.00002125604,0.0001657198,0.0000563274,0.0001278514],"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":[0.00002660189,0.0001547431,0.0004806028,0.00002738169,0.00002987788,0.00001670735,0.02353364,0.00004173649,0.02484581,0.8723765,0.04282476,0.03564165],"study_design_scores_gemma":[0.00002993176,0.00003685065,0.00005690592,0.00002868673,0.00001141191,3.648207e-7,0.007766237,0.003898774,0.0689714,0.001958403,0.9170486,0.0001924478],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05470203,0.0003816808,0.01280161,0.0117519,0.002279131,0.0004305606,0.00000621663,0.0006246172,0.9170222],"genre_scores_gemma":[0.942459,0.00004289704,0.0004003587,0.0002927603,0.0002485451,0.00003954163,0.000006064977,0.0000167708,0.05649402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.887757,"threshold_uncertainty_score":0.9979199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1398464070460439,"score_gpt":0.4250383160193396,"score_spread":0.2851919089732957,"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."}}