{"id":"W2398442511","doi":"","title":"Machine Learning for Information Retrieval: TREC 2009 Web, Relevance Feedback and Legal Tracks.","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Relevance feedback; Relevance (law); Computer science; Information retrieval; World Wide Web; Artificial intelligence; Image retrieval","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":[0.04294441,0.00713612,0.00601841,0.01175779,0.005006247,0.008041848,0.01106023,0.006583819,0.03501713],"category_scores_gemma":[0.05469565,0.001867851,0.002666465,0.009603686,0.003008152,0.009315133,0.003619086,0.00803405,0.04139151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01392838,"about_ca_system_score_gemma":0.02084981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1477941,"about_ca_topic_score_gemma":0.2259757,"domain_scores_codex":[0.9685416,0.01216241,0.002645785,0.00267852,0.01176653,0.002205259],"domain_scores_gemma":[0.9412021,0.01116694,0.002792121,0.009816088,0.02919005,0.00583263],"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":[0.000110193,0.0001289685,0.0002387221,0.0003786245,0.00004450588,0.00004233849,0.00003043275,0.001196094,0.0008723325,0.0004750743,0.9706307,0.02585205],"study_design_scores_gemma":[0.001657632,0.001388001,0.02110289,0.001806864,0.000339793,0.0007405917,0.0005541908,0.06408593,0.0202969,0.02397588,0.8630955,0.0009559168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01853953,0.04164058,0.1037664,0.04337336,0.01670107,0.01538542,0.5763234,0.07375421,0.110516],"genre_scores_gemma":[0.03257002,0.007044063,0.1328794,0.006505889,0.003103672,0.008215744,0.7372357,0.006246088,0.06619939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1477941,"threshold_uncertainty_score":0.2938676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06583608472360887,"score_gpt":0.3616941536988756,"score_spread":0.2958580689752668,"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."}}