{"id":"W2099994538","doi":"","title":"Structured ranking learning using cumulative distribution networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Learning to rank; Computer science; Ranking (information retrieval); Pairwise comparison; Benchmark (surveying); Rank (graph theory); Machine learning; Probabilistic logic; Object (grammar); Artificial intelligence; Independence (probability theory); Cumulative distribution function; Class (philosophy); Data mining; Graphical model; Mathematics; Statistics","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":[0.002674967,0.0009035156,0.001398875,0.002235277,0.0005762447,0.001549036,0.001625169,0.001587712,0.002692797],"category_scores_gemma":[0.01264599,0.0004302243,0.0006351446,0.001716312,0.00123844,0.003198293,0.001175685,0.001478202,0.0005938046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00217922,"about_ca_system_score_gemma":0.001035714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004851186,"about_ca_topic_score_gemma":0.007168787,"domain_scores_codex":[0.9986626,0.000676568,0.00004439092,0.0002298228,0.0002896167,0.00009692891],"domain_scores_gemma":[0.9939387,0.004578121,0.0004914919,0.0003035451,0.000525818,0.0001622892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006488877,0.00006124574,0.001463405,0.00007572503,0.00004480949,0.0000572897,0.0000460688,0.8795409,0.0003547261,0.05118148,0.002493256,0.06461628],"study_design_scores_gemma":[0.000007453741,0.00001609488,0.0001210144,0.000006118336,0.00000390938,0.000008863117,0.000004443058,0.9625602,0.00008511746,0.03691658,0.0002646395,0.000005598103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02990159,0.0006309747,0.9653003,0.0006894292,0.00004041044,0.00006874192,0.0002513319,0.0005808577,0.00253641],"genre_scores_gemma":[0.8527337,0.0008007548,0.1375732,0.0004201033,0.0001854823,0.0003198692,0.001051105,0.0001070151,0.006808728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004851186,"threshold_uncertainty_score":0.01581144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773856443456836,"score_gpt":0.2659914035239402,"score_spread":0.2282528390893719,"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."}}