{"id":"W2034707531","doi":"10.1145/2009916.2009941","title":"CRTER","year":2011,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Term (time); Computer science; Term Discrimination; Information retrieval; Weighting; Intersection (aeronautics); Boosting (machine learning); Query expansion; Probabilistic logic; Ranking (information retrieval); Divergence-from-randomness model; Data mining; Artificial intelligence; Search engine; Web search query; Concept search; Geography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001602752,0.0007827926,0.0009716104,0.002370279,0.0006422529,0.001823142,0.001843991,0.001411153,0.01717475],"category_scores_gemma":[0.005889555,0.0002589648,0.0007456768,0.002915987,0.0005137327,0.00347461,0.001479595,0.0009393159,0.01404571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104263,"about_ca_system_score_gemma":0.00108847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002819436,"about_ca_topic_score_gemma":0.004026631,"domain_scores_codex":[0.9982621,0.0003165247,0.000100727,0.0004388756,0.0007320479,0.0001497603],"domain_scores_gemma":[0.9977004,0.0004883154,0.0001884653,0.0009232486,0.0006138349,0.00008566953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004967991,0.0003652504,0.003128601,0.0006282427,0.0001298381,0.000249077,0.0001910222,0.04215095,0.0361,0.09048936,0.0818833,0.7441875],"study_design_scores_gemma":[0.0001361466,0.0006123383,0.005029314,0.00009360577,0.0001733214,0.001699964,0.0001413842,0.6345516,0.04314195,0.08934874,0.2248807,0.0001909215],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04093836,0.004693088,0.8531859,0.001602089,0.000574297,0.0008841961,0.006100317,0.0164406,0.07558117],"genre_scores_gemma":[0.4053974,0.002716409,0.4673917,0.001168624,0.0005626392,0.0006656891,0.01144971,0.001391773,0.109256],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9828252,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07215051758748364,"score_gpt":0.2336056618730842,"score_spread":0.1614551442856006,"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."}}