{"id":"W2952728573","doi":"10.48550/arxiv.1208.5172","title":"An iterative scheme for solving the optimal transportation problem","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Institute for the Mathematical Sciences; University of British Columbia","funders":"","keywords":"Measure (data warehouse); Scheme (mathematics); Mathematical optimization; Transportation theory; Function (biology); Upper and lower bounds; Iterative method; Computer science; Mathematics","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":[0.001787989,0.0006855263,0.0005614008,0.0005567067,0.0005826601,0.000758768,0.001569184,0.001788371,0.002925748],"category_scores_gemma":[0.005559853,0.0003428665,0.0008533509,0.0006547334,0.001336827,0.001490961,0.002313007,0.00188717,0.0005717253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001081774,"about_ca_system_score_gemma":0.001490621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003261908,"about_ca_topic_score_gemma":0.002400561,"domain_scores_codex":[0.9993031,0.0002564884,0.00003716486,0.0000983812,0.0002447311,0.00006016768],"domain_scores_gemma":[0.998984,0.0005216531,0.00007823367,0.0001591946,0.0001925728,0.00006431577],"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":[0.00006865711,0.00004091126,0.0002721658,0.00006601305,0.00001799238,0.00005688791,0.0001325531,0.7768676,0.004374993,0.1827714,0.001077106,0.03425371],"study_design_scores_gemma":[0.000008934314,0.00001841281,0.00001526374,0.000005572017,0.000002504638,0.00001188393,0.000005805109,0.9829205,0.000644982,0.01541083,0.0009507734,0.000004654596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003341455,0.00003013492,0.994915,0.0000679896,0.00001736181,0.00002750213,0.00001242693,0.000057923,0.001530126],"genre_scores_gemma":[0.1997891,0.0001430947,0.7960412,0.00005699671,0.00002325967,0.0003075962,0.00008572432,0.00007428853,0.003478828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003261908,"threshold_uncertainty_score":0.009787619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05555063535320044,"score_gpt":0.1962799357954532,"score_spread":0.1407293004422528,"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."}}