{"id":"W4323319191","doi":"10.3390/proteomes11010010","title":"Optimized Proteome Reduction for Integrative Top–Down Proteomics","year":2023,"lang":"en","type":"article","venue":"Proteomes","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proteome; Dithiothreitol; Reduction (mathematics); Proteomics; Protocol (science); Computer science; Chemistry; Computational biology; Biochemistry; Biology; Enzyme; Medicine; Mathematics","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":[0.002047429,0.001549358,0.001145802,0.0007637303,0.0006314938,0.0009241962,0.001225326,0.0008364278,0.001860901],"category_scores_gemma":[0.001847459,0.0006733878,0.0007596224,0.00107715,0.0005327503,0.00069998,0.0009729596,0.002446331,0.002110844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004892775,"about_ca_system_score_gemma":0.0008778215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007370116,"about_ca_topic_score_gemma":0.002289219,"domain_scores_codex":[0.9976097,0.0005523451,0.000219532,0.0005084997,0.0008734899,0.0002363947],"domain_scores_gemma":[0.9992911,0.000180502,0.00008244091,0.0001482612,0.0002515679,0.00004610481],"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":[0.00004289124,0.0000462784,0.00005182031,0.0002231425,0.0000220512,0.00004387016,0.00003942348,0.0002543691,0.9927659,0.0002983171,0.0008015826,0.005410383],"study_design_scores_gemma":[0.00001244055,0.0001223406,0.0010187,0.00003070575,0.00003136561,0.0002823653,0.0000282689,0.00294053,0.9693156,0.0003538608,0.02582376,0.00004003051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1226522,0.005302611,0.8548749,0.0006772161,0.000687902,0.00200723,0.004331605,0.003844552,0.005621638],"genre_scores_gemma":[0.108784,0.007524482,0.8605622,0.0005108089,0.0001204522,0.002396377,0.01268559,0.001061208,0.006354804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002047429,"threshold_uncertainty_score":0.01082802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907385947959687,"score_gpt":0.3078943647640642,"score_spread":0.2888205052844673,"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."}}