{"id":"W2166538692","doi":"10.1186/1477-5956-9-s1-s3","title":"Peptide charge state determination of tandem mass spectra from low-resolution collision induced dissociation","year":2011,"lang":"en","type":"article","venue":"Proteome Science","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Tandem; Tandem mass spectrometry; Mass spectrum; Spectral line; Collision-induced dissociation; Mass spectrometry; Chemistry; False positive paradox; Dissociation (chemistry); Collision; Gaussian; Analytical Chemistry (journal); Atomic physics; Computer science; Computational physics; Physics; Artificial intelligence; Materials science; Computational chemistry; Chromatography; Physical chemistry","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":[0.0009247824,0.0004134225,0.0003340559,0.001409658,0.0002642368,0.0005613217,0.0005544747,0.0005086202,0.0007059039],"category_scores_gemma":[0.002582077,0.0001475119,0.0002736103,0.0008037178,0.0003583929,0.0006268495,0.0003630856,0.0004119364,0.0004140094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003910569,"about_ca_system_score_gemma":0.0002631585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004240948,"about_ca_topic_score_gemma":0.0006170639,"domain_scores_codex":[0.9996547,0.00006006648,0.00002267749,0.00008364079,0.0001566388,0.00002240607],"domain_scores_gemma":[0.9989356,0.0004162848,0.000231776,0.00009331477,0.0002798147,0.00004319877],"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":[0.001164532,0.0002280602,0.04127855,0.0003702821,0.0001223349,0.0002970503,0.0001349403,0.01622405,0.6355476,0.001854314,0.001036015,0.3017423],"study_design_scores_gemma":[0.00005137729,0.0002482975,0.0456835,0.00002868325,0.00009199735,0.001920691,0.00007734728,0.5265079,0.4188683,0.004670445,0.001768193,0.00008327246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5845483,0.0007075668,0.4123895,0.0001026593,0.00002405707,0.00006758192,0.000280803,0.0008432048,0.001036366],"genre_scores_gemma":[0.8020464,0.00027773,0.1966138,0.00004829981,0.00001320435,0.0000387785,0.0005204159,0.00003557756,0.0004058215],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001409658,"threshold_uncertainty_score":0.00489074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02345625172092494,"score_gpt":0.272944576326517,"score_spread":0.2494883246055921,"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."}}