{"id":"W2158921918","doi":"","title":"York University at TREC 2006: Legal Track","year":2006,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Weighting; Track (disk drive); Computer science; Term (time); Information retrieval; Text retrieval; Probabilistic logic; Order (exchange); Domain (mathematical analysis); Artificial intelligence; Data mining; Mathematics","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":[0.01109814,0.001938343,0.002205171,0.005467698,0.005707731,0.0045507,0.002163598,0.002484835,0.08291732],"category_scores_gemma":[0.01513043,0.0007332283,0.0005767519,0.003746538,0.0008886682,0.004391031,0.002129284,0.003483285,0.04381429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006312004,"about_ca_system_score_gemma":0.009969831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1762652,"about_ca_topic_score_gemma":0.2990222,"domain_scores_codex":[0.9958155,0.0007755524,0.0002173505,0.0004701847,0.002274316,0.0004470257],"domain_scores_gemma":[0.9808049,0.002237099,0.0005666942,0.001924244,0.01101501,0.003452141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008807661,0.000183829,0.0003501901,0.00009381118,0.00001021606,0.00001871816,0.00003727614,0.0002293827,0.0006478085,0.0006220058,0.9780027,0.0197158],"study_design_scores_gemma":[0.000405302,0.000305985,0.008146981,0.000110387,0.00005350627,0.00008956446,0.0003102687,0.008266073,0.007013247,0.002076996,0.9730878,0.000133759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05352223,0.009159159,0.02068273,0.03434412,0.02130865,0.007707844,0.4914412,0.03840964,0.3234245],"genre_scores_gemma":[0.04055806,0.001941746,0.02726431,0.002580608,0.001415749,0.001850631,0.5772915,0.002203571,0.3448938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1762652,"threshold_uncertainty_score":0.3504785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403716352911581,"score_gpt":0.185078277114786,"score_spread":0.1710411135856702,"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."}}