{"id":"W3096465006","doi":"10.1101/2020.11.03.365585","title":"Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Workflow; Software; Benchmarking; Proteome; Data mining; Proteomics; Mascot; Bioinformatics; Database; Chemistry; Biology; Programming language","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":[0.02756499,0.00270373,0.001421729,0.005796214,0.001869672,0.003152629,0.003365454,0.001373453,0.004267981],"category_scores_gemma":[0.03610148,0.001299618,0.00164286,0.003936123,0.000950586,0.00245669,0.004427314,0.001968988,0.005446769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416078,"about_ca_system_score_gemma":0.002071438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001416331,"about_ca_topic_score_gemma":0.00111661,"domain_scores_codex":[0.9858724,0.002403544,0.002877426,0.002646712,0.005388749,0.0008111927],"domain_scores_gemma":[0.9747615,0.0068292,0.001366022,0.007739379,0.008580461,0.0007233701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006290434,0.001844914,0.0342169,0.003292419,0.001328566,0.0007246386,0.001393253,0.03126273,0.5074599,0.007565706,0.04722575,0.3573947],"study_design_scores_gemma":[0.0002974953,0.0009701872,0.03273057,0.0004308893,0.0002278284,0.000752372,0.000367431,0.117395,0.7749765,0.00485947,0.0664268,0.0005655227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3456796,0.003047816,0.5175011,0.0008258393,0.0007293269,0.002418969,0.02701116,0.09329914,0.00948703],"genre_scores_gemma":[0.2648324,0.001545574,0.6470722,0.0004217497,0.00008455257,0.004143707,0.05816646,0.02098328,0.002750114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02756499,"threshold_uncertainty_score":0.1457793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04385771150127737,"score_gpt":0.3182112711060815,"score_spread":0.2743535596048041,"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."}}