{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008568047,0.001404985,0.002007748,0.001217598,0.002315802,0.00182224,0.001772248,0.002179933,0.01051766],"category_scores_gemma":[0.03576838,0.0008393283,0.000623906,0.001392038,0.001140023,0.003612309,0.002661247,0.001929989,0.005379235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001771538,"about_ca_system_score_gemma":0.002241026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02260366,"about_ca_topic_score_gemma":0.02748036,"domain_scores_codex":[0.9902115,0.005469103,0.001065552,0.001059611,0.00142884,0.0007653964],"domain_scores_gemma":[0.9631801,0.02566348,0.0007810307,0.004159362,0.004155301,0.002060684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.04542645,0.04746341,0.03443783,0.01084656,0.001347559,0.005599123,0.02364468,0.0339103,0.2488852,0.009196055,0.2392327,0.3000102],"study_design_scores_gemma":[0.02602796,0.06084327,0.128294,0.0005764834,0.0017451,0.004688042,0.01769367,0.2399847,0.2828802,0.01459677,0.2203735,0.002296353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542228,0.001119643,0.008795639,0.0007306598,0.0002851544,0.003068848,0.006798586,0.00397046,0.02100823],"genre_scores_gemma":[0.9250637,0.000390365,0.03080378,0.001482073,0.0002386643,0.003398646,0.019966,0.00112031,0.01753642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02260366,"threshold_uncertainty_score":0.0453127,"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."}}