{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001775072,0.000166224,0.0001915131,0.00003098228,0.0001010394,0.0000365004,0.0001828911,0.00007450925,0.00002119247],"category_scores_gemma":[0.00003530295,0.0001500143,0.00003840346,0.0001029672,0.00005580168,0.000001715295,0.0002508033,0.0001042068,0.00001079881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001393949,"about_ca_system_score_gemma":0.00004388984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002865119,"about_ca_topic_score_gemma":0.00005249215,"domain_scores_codex":[0.9989904,0.00003788961,0.0002191347,0.0003625494,0.00008819725,0.0003018348],"domain_scores_gemma":[0.9995137,0.00002986604,0.00008455011,0.0002892973,0.00004824742,0.00003433104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001213451,0.00004912809,0.6881686,0.00007434642,0.0001159769,0.00001279474,0.001094817,0.007208216,0.2920177,0.0007497145,0.0002568869,0.01013042],"study_design_scores_gemma":[0.005601116,0.003423351,0.5656085,0.0004713651,0.0000580273,0.0003228373,0.008270719,0.005741315,0.3130781,0.00120983,0.09346095,0.002753892],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904624,0.0007231232,0.0002381354,0.0001153981,0.000303146,0.0001730627,0.000002252462,0.000007808301,0.007974709],"genre_scores_gemma":[0.9918396,0.00004102997,0.007169378,0.0002264721,0.0002821137,0.000005179714,0.0000113618,0.0000232815,0.0004015504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1225601,"threshold_uncertainty_score":0.6117404,"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."}}