{"id":"W2157365731","doi":"","title":"Thomson Legal and Regulatory at NTCIR-4: Monolingual and Pivot-Language Retrieval Experiments","year":2004,"lang":"en","type":"article","venue":"NTCIR","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Computer science; Task (project management); Natural language processing; Artificial intelligence; Information retrieval; Relevance (law); Quality (philosophy)","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":[0.0002235967,0.0001441779,0.0001438096,0.000099543,0.0002349978,0.0002008759,0.0002597301,0.00008910555,0.00001878079],"category_scores_gemma":[0.00003130196,0.0001295309,0.0000340088,0.0001820886,0.0001023738,0.0007516192,0.0003490822,0.0001455532,0.00004324795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001106869,"about_ca_system_score_gemma":0.00008048656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005993811,"about_ca_topic_score_gemma":0.000005248009,"domain_scores_codex":[0.9987648,0.00002815477,0.00021279,0.0002847017,0.0004074857,0.0003021359],"domain_scores_gemma":[0.9993544,0.0000215575,0.00006418684,0.0003199291,0.0000529666,0.0001870107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009834555,0.0007501424,0.01100977,0.0003235037,0.0001496547,0.001592751,0.2031729,0.0001221161,0.3908669,0.1758503,0.00223,0.2129485],"study_design_scores_gemma":[0.008093812,0.0009906252,0.2246111,0.0001387633,0.00003345912,0.0008721516,0.001804857,0.005377635,0.7395002,0.001448106,0.01560026,0.001529016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953567,0.0005518708,0.002293076,0.0002329698,0.0001903023,0.0001728291,0.000004596359,0.000120141,0.00107748],"genre_scores_gemma":[0.9957005,0.0000237252,0.002954324,0.0002825939,0.00006286702,0.000003920346,0.00000439112,0.000008118078,0.0009595546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3486332,"threshold_uncertainty_score":0.5282117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01333883386205846,"score_gpt":0.2747150190288032,"score_spread":0.2613761851667447,"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."}}