{"id":"W7100164479","doi":"","title":"E.: Automatic translation of court judgments","year":2008,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Context (archaeology); Focus (optics); Legal translation; Machine translation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002326995,0.00003325086,0.00006889852,0.00002711087,0.0002048705,0.000004881913,0.0001055571,0.00004026014,0.002466474],"category_scores_gemma":[0.00005617924,0.00003142626,0.00003253362,0.000147427,0.0003158211,0.0001442311,0.000004107778,0.00002695329,0.0001435348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000221481,"about_ca_system_score_gemma":0.00007109916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857559,"about_ca_topic_score_gemma":0.0009793256,"domain_scores_codex":[0.9993207,0.00005027275,0.0001684197,0.00006526903,0.0002803546,0.0001149898],"domain_scores_gemma":[0.9997104,0.00008587568,0.00004297566,0.00007015014,0.0000517044,0.00003894205],"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":[0.00002000615,0.0004971244,0.043718,0.00003462283,0.00005764521,0.00001262626,0.1892475,0.000210883,0.005684373,0.4181204,0.01301185,0.329385],"study_design_scores_gemma":[0.0009189352,0.0005841237,0.04059535,0.0002262958,0.0001401654,0.00001357261,0.06034562,0.07447097,0.2691059,0.1278438,0.4239012,0.00185412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6880091,0.00002386631,0.004133102,0.001003127,0.0001933934,0.0001645373,8.737379e-7,0.00009292568,0.3063791],"genre_scores_gemma":[0.9960135,0.00003048089,0.002629069,0.00005680358,0.00004088487,0.000002684103,5.234486e-7,0.000002974583,0.001223056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4108894,"threshold_uncertainty_score":0.9984454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1417101292027702,"score_gpt":0.3748972863804501,"score_spread":0.2331871571776799,"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."}}