{"id":"W1665115054","doi":"10.48550/arxiv.1106.1925","title":"Ranking via Sinkhorn Propagation","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Normalization (sociology); Ranking (information retrieval); Operator (biology); Rank (graph theory); Learning to rank; Range (aeronautics); Projection (relational algebra); Mathematical optimization; Permutation (music); Artificial intelligence; Algorithm; Mathematics; 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":[0.002703062,0.001173401,0.001217337,0.001792608,0.0007800147,0.00168318,0.00153365,0.001541261,0.004554872],"category_scores_gemma":[0.01055158,0.0006064103,0.0007707506,0.001745611,0.001208122,0.00355086,0.001583262,0.001421321,0.001812857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101337,"about_ca_system_score_gemma":0.001186081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002385073,"about_ca_topic_score_gemma":0.004679208,"domain_scores_codex":[0.9986349,0.0004226746,0.00007490078,0.000245682,0.0005007869,0.0001210558],"domain_scores_gemma":[0.9950868,0.002689159,0.000408674,0.00059373,0.00108924,0.0001324608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000240716,0.000209644,0.002408221,0.0002518696,0.0001064484,0.0001590644,0.0002136718,0.4812985,0.00945775,0.08538656,0.0125273,0.4077403],"study_design_scores_gemma":[0.00001441344,0.00005551085,0.0002229093,0.00001525125,0.00001075727,0.00004025753,0.00001670412,0.9533708,0.003058711,0.04161457,0.001566617,0.00001336258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01227298,0.0001808336,0.9837672,0.0002379628,0.00004324822,0.00007284094,0.0001411971,0.0009628742,0.002320888],"genre_scores_gemma":[0.3863737,0.0004939122,0.5963202,0.0005669674,0.0001523231,0.0003794141,0.0010029,0.0005161554,0.01419447],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004554872,"threshold_uncertainty_score":0.01523757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100974059935494,"score_gpt":0.1895552633344075,"score_spread":0.08858120339891352,"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."}}