{"id":"W2896168333","doi":"10.1177/1094342018803672","title":"New BSP/CGM algorithms for spanning trees","year":2018,"lang":"en","type":"article","venue":"The International Journal of High Performance Computing Applications","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Spanning tree; Parallel algorithm; Algorithm; Graph; Computation; Bulk synchronous parallel; Ranking (information retrieval); Theoretical computer science; Parallel computing; Mathematics; Artificial intelligence; Combinatorics","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":[0.0007925315,0.001092733,0.0008504535,0.001719835,0.001003543,0.001060587,0.001846399,0.001140297,0.004290758],"category_scores_gemma":[0.003898412,0.0006267829,0.0009143868,0.002319115,0.0005846529,0.002336787,0.001791886,0.001764412,0.001451255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152293,"about_ca_system_score_gemma":0.001779764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00291671,"about_ca_topic_score_gemma":0.004706621,"domain_scores_codex":[0.9992969,0.000126779,0.00005224505,0.0001618897,0.0002895866,0.0000724821],"domain_scores_gemma":[0.9991533,0.0002533088,0.00009482371,0.0002023716,0.0002312903,0.00006491613],"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":[0.000258941,0.0002622785,0.00101209,0.0005050385,0.00008979005,0.0001859975,0.0002392655,0.2613067,0.0129663,0.1200013,0.02335884,0.5798134],"study_design_scores_gemma":[0.00007340185,0.00005885616,0.0002029576,0.00002301929,0.00002368299,0.0001223177,0.00003810018,0.9271622,0.003509506,0.05864145,0.0101265,0.00001814598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008800978,0.0005469406,0.9836669,0.0002443694,0.0001262851,0.0001609991,0.0001746577,0.001714355,0.004564591],"genre_scores_gemma":[0.05230112,0.0002337023,0.9438682,0.0001171147,0.00007392513,0.0002626348,0.0004487738,0.0002349096,0.0024596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004290758,"threshold_uncertainty_score":0.01435405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186813572026007,"score_gpt":0.3448644533184474,"score_spread":0.3129963175981873,"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."}}