{"id":"W3096134169","doi":"10.1101/2020.10.29.361402","title":"A Python-based optimization framework for high-performance genomics","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Python (programming language); Scalability; Implementation; Software; Usability; Programming language; Programmer; USable; Theoretical computer science; Database; Operating system","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":[0.004439584,0.001822329,0.001057813,0.0009900107,0.0009342596,0.002063946,0.004116572,0.001025097,0.01637203],"category_scores_gemma":[0.006916515,0.001003328,0.002120894,0.001215089,0.001917818,0.002376943,0.003997642,0.004125136,0.009308759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422234,"about_ca_system_score_gemma":0.00439543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005041744,"about_ca_topic_score_gemma":0.004237267,"domain_scores_codex":[0.9972231,0.0006786867,0.0002276904,0.0003382289,0.001219366,0.0003129669],"domain_scores_gemma":[0.9976433,0.0009522269,0.0001918121,0.0004284057,0.0005522684,0.0002318906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001318596,0.0007427894,0.005614486,0.001423195,0.0004845875,0.0008922441,0.0005588678,0.3068124,0.02959513,0.2427412,0.1619804,0.247836],"study_design_scores_gemma":[0.0002639986,0.0000830307,0.0008334499,0.00009311599,0.00004096551,0.000219503,0.00004120802,0.7992011,0.01524829,0.09042168,0.09342466,0.0001290691],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001486696,0.00009943939,0.9262198,0.0002125667,0.0000727372,0.0001258009,0.0006624461,0.0681624,0.002958014],"genre_scores_gemma":[0.0667914,0.0003086184,0.8885702,0.0006524815,0.0001088838,0.001092179,0.003824561,0.0324854,0.00616627],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01637203,"threshold_uncertainty_score":0.05476987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01337514830085307,"score_gpt":0.2151721670907982,"score_spread":0.2017970187899451,"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."}}