{"id":"W78303509","doi":"","title":"Subdivision of Edges and Matching Size.","year":2007,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Subdivision; Mathematics; Matching (statistics); Combinatorics; Statistics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003932939,0.00005641993,0.00008489117,0.00003381597,0.00007223133,0.00004296463,0.0003309706,0.00002716572,0.000002077326],"category_scores_gemma":[0.00003374066,0.00005333349,0.00001511874,0.0001921873,0.00003111328,0.0002046144,0.0002082256,0.00005644388,0.000006515795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007394094,"about_ca_system_score_gemma":0.00001197069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003624956,"about_ca_topic_score_gemma":0.000001513713,"domain_scores_codex":[0.9994379,0.000009190485,0.00013954,0.0001688703,0.0001211018,0.0001234151],"domain_scores_gemma":[0.9993187,0.0002226834,0.00005832405,0.0003076792,0.00003835295,0.00005423505],"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":[7.073768e-7,0.00003491539,0.0007232788,0.000005382009,0.000002993011,0.000001515685,0.0002929969,1.586746e-7,0.0008766915,0.9287688,0.0002931056,0.0689995],"study_design_scores_gemma":[0.0005404026,0.000105511,0.09399623,0.00004628158,0.000006414939,0.00001319934,0.0001179933,0.003283044,0.01842877,0.8800854,0.003169937,0.0002067813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301236,0.000123989,0.06768318,0.0001710548,0.0004306136,0.00006584433,0.00000253908,0.0000670538,0.00133214],"genre_scores_gemma":[0.9708799,0.00001350221,0.02901117,0.00003610385,0.000003916214,0.000001685504,0.000001180352,0.000003731213,0.00004886178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09327295,"threshold_uncertainty_score":0.2174877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127283910301276,"score_gpt":0.2550355195279408,"score_spread":0.2437626804249281,"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."}}