{"id":"W2137594903","doi":"10.1109/ipps.1999.760446","title":"Coarse grained parallel maximum matching in convex bipartite graphs","year":2003,"lang":"en","type":"article","venue":"","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Bipartite graph; Combinatorics; Binary logarithm; Computation; Matching (statistics); Regular polygon; Mathematics; Graph; Computational complexity theory; Algorithm; 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":[0.0006823539,0.0006491523,0.001193137,0.0007307767,0.0009567888,0.0008976049,0.001680694,0.0007727668,0.004353974],"category_scores_gemma":[0.002064044,0.000575007,0.0007109071,0.001704274,0.0009143153,0.002352165,0.001966804,0.0008148309,0.001081485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353411,"about_ca_system_score_gemma":0.0009784633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006475777,"about_ca_topic_score_gemma":0.007094087,"domain_scores_codex":[0.9991896,0.0002183304,0.00003713499,0.0002089855,0.0001961431,0.0001498248],"domain_scores_gemma":[0.9992071,0.0003051799,0.00008188301,0.0002630742,0.0000879385,0.00005493969],"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":[0.0005136004,0.0001649552,0.0008550458,0.0002204755,0.00006739432,0.0001494786,0.0001788114,0.7480419,0.01164276,0.0516426,0.005495122,0.1810278],"study_design_scores_gemma":[0.00003280214,0.00002859779,0.0001439625,0.000004072246,0.000008872516,0.00002965931,0.00002621712,0.9513328,0.002651287,0.04434001,0.0013958,0.000006074606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05144784,0.0001941205,0.9411404,0.0002162583,0.00002224161,0.0001135611,0.0001642676,0.001522848,0.005178424],"genre_scores_gemma":[0.4099148,0.0001418612,0.5855584,0.00009118025,0.00002487671,0.0001945921,0.000541502,0.0002110112,0.003321846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006475777,"threshold_uncertainty_score":0.01456547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248653252835489,"score_gpt":0.2465879669221275,"score_spread":0.2241014343937726,"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."}}