{"id":"W2963782874","doi":"10.4230/lipics.icalp.2017.55","title":"Further Approximations for Demand Matching: Matroid Constraints and Minor-Closed Graphs","year":2017,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematics; Approximation algorithm; Combinatorics; Matroid; Discrete mathematics; Rounding; Polynomial-time approximation scheme; Vertex (graph theory); Linear programming relaxation; Knapsack problem; Graph; Mathematical optimization; Linear programming; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002449598,0.001835408,0.001706324,0.001040117,0.001029003,0.003244315,0.003462288,0.002262274,0.01026856],"category_scores_gemma":[0.01538233,0.0007902819,0.00195914,0.002868643,0.001299328,0.007388088,0.002593872,0.005375813,0.001422693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002693264,"about_ca_system_score_gemma":0.001591601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003864516,"about_ca_topic_score_gemma":0.004461896,"domain_scores_codex":[0.9978192,0.0006862885,0.00008300643,0.0005176305,0.0005527135,0.0003410834],"domain_scores_gemma":[0.9935575,0.003364038,0.0006609528,0.001638752,0.0004141931,0.000364556],"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":[0.0008035374,0.0007277465,0.002439215,0.0005478225,0.0001358571,0.0003523887,0.0008610075,0.4997457,0.008182337,0.3554545,0.01628004,0.1144698],"study_design_scores_gemma":[0.00005562301,0.00008809794,0.0002905069,0.00005004985,0.00002781627,0.000155616,0.0001596019,0.782726,0.001740083,0.2087376,0.005952809,0.00001615419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08736105,0.001414511,0.8860344,0.002698142,0.0001457496,0.0001722295,0.0007475711,0.001106318,0.02031997],"genre_scores_gemma":[0.5240769,0.0009630358,0.4611358,0.001163713,0.0002832048,0.0002584748,0.001429,0.0006800449,0.01000975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01026856,"threshold_uncertainty_score":0.03435177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349403411633295,"score_gpt":0.2828776831977924,"score_spread":0.2593836490814594,"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."}}