{"id":"W1660572054","doi":"10.1051/m2an/2015033","title":"Numerical methods for matching for teams and Wasserstein barycenters","year":2015,"lang":"en","type":"article","venue":"ESAIM Mathematical Modelling and Numerical Analysis","topic":"Geometric Analysis and Curvature Flows","field":"Mathematics","cited_by":91,"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; Applied 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":[0.003189224,0.0009852505,0.001115379,0.001765916,0.0008338793,0.001613987,0.001814754,0.002234611,0.005464675],"category_scores_gemma":[0.01331258,0.0005939784,0.001097671,0.001474795,0.002173732,0.002384061,0.003170101,0.002193997,0.0009910441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746513,"about_ca_system_score_gemma":0.001751348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004878289,"about_ca_topic_score_gemma":0.003718928,"domain_scores_codex":[0.9990244,0.0004590706,0.00005772977,0.0001327274,0.0002547385,0.0000712648],"domain_scores_gemma":[0.9967567,0.001975852,0.0003782787,0.0002214746,0.0004730003,0.0001945667],"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":[0.00004525671,0.00003698955,0.0005164811,0.0001293903,0.00003600384,0.00005172542,0.00009128716,0.6377122,0.0009834744,0.3267823,0.001888121,0.03172674],"study_design_scores_gemma":[0.000006721861,0.000007134833,0.00004200646,0.00001391924,0.000002724916,0.00001074708,0.00001006975,0.9339866,0.0001432448,0.06431432,0.001455407,0.000007167466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001853729,0.0003287918,0.9952759,0.0002197907,0.0000777423,0.00002764904,0.00002651082,0.0000816805,0.00210831],"genre_scores_gemma":[0.2087348,0.001029164,0.7741457,0.0002943684,0.0002121116,0.0005321566,0.0002185337,0.0003996795,0.01443354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005464675,"threshold_uncertainty_score":0.01828116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07819214140428939,"score_gpt":0.3681187796123893,"score_spread":0.2899266382080999,"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."}}