{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003111621,0.0002111822,0.0001963881,0.0002807199,0.0001888048,0.0001481292,0.001426067,0.0002329551,0.00006751197],"category_scores_gemma":[0.00001746133,0.0002249446,0.0001543405,0.0004916166,0.00006898494,0.0008209433,0.001304281,0.0005138493,0.0003922268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001567726,"about_ca_system_score_gemma":0.0001814497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001087654,"about_ca_topic_score_gemma":0.000008057721,"domain_scores_codex":[0.9987226,0.00009170674,0.0002036163,0.0005466917,0.0001359596,0.0002994383],"domain_scores_gemma":[0.9985375,0.00002605496,0.0002238825,0.0008020629,0.0002800626,0.0001304118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001200419,0.0002982292,0.003574438,0.0003136004,0.0001309976,0.0005263006,0.003729965,0.02766639,0.000488341,0.928011,0.0004699066,0.03467077],"study_design_scores_gemma":[0.0006589247,0.0001040308,0.004088652,0.0001047196,0.00006458268,0.00001359407,0.00004875093,0.9174299,0.003322742,0.07247823,0.0008895947,0.000796331],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1289704,0.000009490028,0.8636357,0.00003745,0.0005316574,0.0003228885,0.000003914083,0.0002753346,0.006213206],"genre_scores_gemma":[0.9967715,0.00002596632,0.001701199,0.00007433034,0.00004566749,0.000001053799,0.00002107403,0.000008637173,0.001350581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8897635,"threshold_uncertainty_score":0.9172974,"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."}}