{"id":"W1531964914","doi":"10.5555/2095116.2095241","title":"Weighted capacitated, priority, and geometric set cover via improved quasi-uniform sampling","year":2012,"lang":"en","type":"article","venue":"","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Set cover problem; Mathematics; Approximation algorithm; Covering problems; Cover (algebra); Combinatorics; Set (abstract data type); Sampling (signal processing); Matching (statistics); Binary logarithm; Discrete mathematics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005422659,0.0002003535,0.0002181461,0.0004048901,0.0002629039,0.0002216688,0.0005477627,0.00009422353,0.00008767231],"category_scores_gemma":[0.00004016755,0.0001723678,0.00006871037,0.001414681,0.00008966434,0.001204102,0.0004975629,0.0002267718,0.00008349154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004460589,"about_ca_system_score_gemma":0.00002825458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002594351,"about_ca_topic_score_gemma":0.0000160057,"domain_scores_codex":[0.9984969,0.0000386832,0.0002586082,0.0003412071,0.0002708843,0.0005936674],"domain_scores_gemma":[0.9988452,0.0002250559,0.00008274933,0.0004890666,0.00008921795,0.0002687699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003200038,0.001002702,0.02540887,0.0001817092,0.0002291615,0.00001182714,0.004949743,0.000009085245,0.004193929,0.2918848,0.001993812,0.6701024],"study_design_scores_gemma":[0.002958526,0.0007449312,0.05698523,0.00005596679,0.00006475569,0.0004811495,0.0002969626,0.781005,0.007485669,0.08268398,0.06492465,0.002313169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07807399,0.000359338,0.9181355,0.000206021,0.0006391632,0.0001845285,0.000008485594,0.0002582333,0.002134797],"genre_scores_gemma":[0.7497811,0.00004700492,0.2493604,0.00032787,0.0001378483,0.00000745685,0.000006524027,0.00001065068,0.0003211309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7809959,"threshold_uncertainty_score":0.7028955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678905663808665,"score_gpt":0.2687750987619836,"score_spread":0.2319860421238969,"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."}}