{"id":"W2180283910","doi":"10.1186/1471-2105-9-s6-s13","title":"Quality assessment of peptide tandem mass spectra","year":2008,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mass spectrum; Tandem; Computer science; Tandem mass spectrometry; Quality (philosophy); Spectral line; Classifier (UML); Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Data mining; Mass spectrometry; Biological system; Chemistry; Biology; Chromatography; Materials science; Physics","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":[0.00564713,0.0006848458,0.0007897862,0.003879561,0.0005100505,0.0014493,0.0009736365,0.000936484,0.0009589046],"category_scores_gemma":[0.0105616,0.0001936254,0.0007132915,0.002174743,0.0008000623,0.001328709,0.0007760958,0.0007707686,0.0003991045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006742972,"about_ca_system_score_gemma":0.0004755532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001247661,"about_ca_topic_score_gemma":0.001150715,"domain_scores_codex":[0.9963632,0.0004130999,0.0004000627,0.0005901136,0.002069964,0.0001634755],"domain_scores_gemma":[0.9844204,0.003368226,0.004054809,0.0008321058,0.006997838,0.0003266753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001950043,0.0002987542,0.1657516,0.001665645,0.0006382815,0.001075955,0.0005280368,0.02178681,0.2712535,0.001710229,0.004602035,0.5287392],"study_design_scores_gemma":[0.000103909,0.0009687787,0.2214373,0.00029505,0.0005500966,0.004796397,0.000675844,0.4225653,0.3312096,0.005289678,0.01177572,0.0003323752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4591834,0.005684584,0.5285438,0.0004327679,0.000210867,0.0002310855,0.001362009,0.002074091,0.002277452],"genre_scores_gemma":[0.7972804,0.00119943,0.1982468,0.0001472474,0.0001554476,0.0001015726,0.00191543,0.0001745327,0.0007790537],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00564713,"threshold_uncertainty_score":0.02986521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04926022827379116,"score_gpt":0.3350040443686998,"score_spread":0.2857438160949086,"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."}}