{"id":"W4287977094","doi":"10.5281/zenodo.3466997","title":"DDIA: data dependent-independent acquisition proteomics - DDA and DIA in a single LC-MS/MS run","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Chromatography; Chemistry","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":[0.005661189,0.002031435,0.001392766,0.002071889,0.001005054,0.003137361,0.002277293,0.001343604,0.006277842],"category_scores_gemma":[0.005454008,0.001256708,0.001119323,0.001124884,0.001432984,0.002922587,0.003984052,0.00345303,0.005046166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008720364,"about_ca_system_score_gemma":0.001890947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003810277,"about_ca_topic_score_gemma":0.0007746709,"domain_scores_codex":[0.9955077,0.0006372873,0.000401338,0.001795865,0.001362853,0.0002949071],"domain_scores_gemma":[0.9956424,0.001307075,0.0005066623,0.001527508,0.0007932348,0.00022315],"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":[0.001436231,0.0002954509,0.004694119,0.0008786682,0.0003469058,0.0004231198,0.0002276734,0.00164577,0.8350291,0.00754128,0.01117865,0.1363031],"study_design_scores_gemma":[0.00007974744,0.000353689,0.004956843,0.00005681536,0.00008852375,0.001125674,0.00006147331,0.03093462,0.9207633,0.006206942,0.0352129,0.0001596043],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04007938,0.0009784522,0.9168723,0.0007295724,0.0004745906,0.0005931619,0.002825818,0.03216388,0.005282773],"genre_scores_gemma":[0.1715004,0.0006810193,0.8059134,0.002044012,0.000208065,0.001692839,0.006252043,0.004761292,0.006946996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006277842,"threshold_uncertainty_score":0.02993959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03014079576763502,"score_gpt":0.2578178295858484,"score_spread":0.2276770338182134,"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."}}