{"id":"W2951829998","doi":"10.48550/arxiv.1411.3602","title":"Numerical methods for matching for teams and Wasserstein barycenters","year":2014,"lang":"en","type":"preprint","venue":"Springer Link (Chiba Institute of Technology)","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Matching (statistics); Convergence (economics); Mathematics; Mathematical optimization; Linear programming; Population; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002221164,0.0004164205,0.001406512,0.0007820179,0.0001610333,0.00006528241,0.0007882192,0.00100329,0.000006014878],"category_scores_gemma":[0.0007034153,0.0004761674,0.0003879861,0.0001640209,0.0003072861,0.0001135481,0.0005467746,0.0006779919,0.00001175573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055286,"about_ca_system_score_gemma":0.00004377989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008679588,"about_ca_topic_score_gemma":0.000005471315,"domain_scores_codex":[0.9972779,0.000025045,0.001219963,0.0009751971,0.00003144209,0.0004705264],"domain_scores_gemma":[0.9976706,0.0001431778,0.001153675,0.000881461,0.0000689943,0.00008210468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008050452,0.00007804477,0.003912349,0.002470199,0.0004138952,6.799972e-7,0.0002105707,0.001115073,0.0005312901,0.9609413,0.00009479348,0.03015127],"study_design_scores_gemma":[0.001662483,0.0003132551,0.0003698939,0.0008859768,0.0000959612,0.00001122355,0.00008780906,0.01476051,0.004376899,0.7081404,0.2683058,0.0009898004],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1555505,0.002065573,0.8330061,0.00203017,0.003969945,0.001553447,0.0001558547,0.0002952573,0.001373193],"genre_scores_gemma":[0.6328809,0.00006963118,0.3660401,0.00006612147,0.0002980881,0.0003513021,0.00002841449,0.00006612441,0.0001992714],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4773305,"threshold_uncertainty_score":0.999769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02881363280520622,"score_gpt":0.2864618798603622,"score_spread":0.257648247055156,"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."}}