{"id":"W4382775189","doi":"10.1111/2041-210x.14168","title":"CATE: A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data","year":2023,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Canadian Institutes of Health Research; Alberta Innovates","keywords":"CUDA; Computer science; Scalability; Scale (ratio); Supercomputer; Parallel computing; Graphics processing unit; Software; Computational science; Database","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":[0.002636699,0.002281736,0.001237138,0.001703618,0.001014724,0.002509166,0.005284341,0.001153832,0.02526779],"category_scores_gemma":[0.009761045,0.001269906,0.001783695,0.002332783,0.001086369,0.00230523,0.002343448,0.002544299,0.007174619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190754,"about_ca_system_score_gemma":0.002152024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008481305,"about_ca_topic_score_gemma":0.008580284,"domain_scores_codex":[0.9983737,0.0003965452,0.0001764875,0.0003499341,0.0005394131,0.0001638461],"domain_scores_gemma":[0.9960377,0.001731092,0.0002429762,0.0006665581,0.001065166,0.0002564923],"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":[0.002421074,0.0005707531,0.01088831,0.001713658,0.001580297,0.001496034,0.0007902429,0.1271096,0.02920488,0.03609958,0.3320047,0.4561208],"study_design_scores_gemma":[0.0006891079,0.0001441061,0.002007084,0.00009149595,0.00009089767,0.0002154199,0.0001025805,0.9186984,0.01674045,0.01786925,0.04323003,0.0001211616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01146107,0.0004078196,0.7969153,0.0002736668,0.0003113682,0.0003362226,0.002906471,0.1841339,0.003254085],"genre_scores_gemma":[0.1490077,0.0003689826,0.8134079,0.0005200021,0.0001236996,0.001766217,0.009862601,0.01829546,0.006647457],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02526779,"threshold_uncertainty_score":0.08452916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05249399322437297,"score_gpt":0.3841206627630143,"score_spread":0.3316266695386413,"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."}}