{"id":"W2626157061","doi":"10.29007/hb5r","title":"gcn.MOPS: Accelerating cn.MOPS with GPU","year":2019,"lang":"en","type":"article","venue":"EPiC series in computing","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Speedup; Computer science; Parallel computing; Central processing unit; CPU shielding; Multi-core processor; Acceleration; Process (computing); Single-core; Parallelism (grammar); Computer hardware; Operating system; Physics","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":[0.0005138881,0.001363916,0.0006893129,0.0005417071,0.000512181,0.0007775864,0.00206812,0.0007010545,0.00713909],"category_scores_gemma":[0.001721119,0.0004631269,0.0007918831,0.0009352085,0.000366267,0.0006509227,0.0009541851,0.001162284,0.00263229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009115465,"about_ca_system_score_gemma":0.001729267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01899332,"about_ca_topic_score_gemma":0.01910335,"domain_scores_codex":[0.999676,0.00004324794,0.00001439502,0.00009011539,0.000119419,0.00005679264],"domain_scores_gemma":[0.9996417,0.00009377651,0.00003034995,0.00008361816,0.0001148167,0.00003576549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001277804,0.0003551745,0.008425546,0.001039917,0.0004281122,0.0004355181,0.000401254,0.196269,0.0693953,0.02974422,0.1475118,0.5447163],"study_design_scores_gemma":[0.0001581805,0.0000806909,0.0009012595,0.00002047813,0.00003643759,0.0001104528,0.00002265996,0.9404629,0.01910257,0.004662251,0.0344043,0.0000378094],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03850796,0.0006943368,0.855122,0.0004056699,0.0004375174,0.0002546151,0.001491794,0.09116041,0.0119257],"genre_scores_gemma":[0.1547945,0.0004320923,0.8274221,0.0002982886,0.00005920383,0.0004012259,0.003045004,0.006930817,0.006616812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01899332,"threshold_uncertainty_score":0.0377655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01067306501312257,"score_gpt":0.2276986797527043,"score_spread":0.2170256147395817,"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."}}