{"id":"W2134671338","doi":"10.48550/arxiv.1401.8008","title":"Support vector comparison machines","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ranking (information retrieval); Margin (machine learning); Ranking SVM; Support vector machine; Rank (graph theory); Maximization; Set (abstract data type); Function (biology); Computer science; Artificial intelligence; Machine learning; Data set; Mathematics; Pattern recognition (psychology); Mathematical optimization; Combinatorics","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":[0.006601066,0.00201391,0.003879117,0.002443947,0.0007056692,0.002605857,0.002674768,0.003085461,0.005667591],"category_scores_gemma":[0.01913368,0.0004871571,0.001099511,0.004127634,0.001289056,0.003092531,0.001851583,0.002737346,0.004346608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009038747,"about_ca_system_score_gemma":0.0009175277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007072486,"about_ca_topic_score_gemma":0.0006245307,"domain_scores_codex":[0.9916627,0.004361433,0.0004827823,0.001538516,0.001687413,0.0002671016],"domain_scores_gemma":[0.993475,0.003616871,0.0005972363,0.0009454131,0.00116112,0.0002043545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004150003,0.0003638551,0.002719103,0.0007052318,0.000408987,0.0001130197,0.00009366046,0.1568053,0.001668641,0.07256105,0.02413668,0.7400094],"study_design_scores_gemma":[0.00008731331,0.0004191964,0.0008638357,0.00009215846,0.00005090197,0.0001870461,0.00004972591,0.8605128,0.00205829,0.1226997,0.01292937,0.0000497287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02082299,0.0117019,0.951981,0.001468642,0.001018546,0.0003396062,0.0009186162,0.00218856,0.009560154],"genre_scores_gemma":[0.5526513,0.00415261,0.4260004,0.001034223,0.001734704,0.0006628574,0.003044336,0.0003113011,0.01040831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006601066,"threshold_uncertainty_score":0.0349102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002519894942457,"score_gpt":0.1871198359070462,"score_spread":0.08686784641280047,"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."}}