{"id":"W2499626313","doi":"10.1021/acs.jproteome.6b00392","title":"Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 2.1","year":2016,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Data science; Comparability; Checklist; Computer science; Proteome; Set (abstract data type); Human proteome project; Data quality; Standardization; Identification (biology); Interpretation (philosophy); Information retrieval; Bioinformatics; Chemistry; Proteomics; Psychology; Biology; Service (business); Business; Ecology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1149612,0.002062935,0.002974706,0.01742365,0.00297064,0.008406353,0.0112,0.007502499,0.04621484],"category_scores_gemma":[0.1914388,0.002851204,0.002672021,0.01174607,0.002706044,0.00553405,0.005771402,0.007762644,0.06518807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003319915,"about_ca_system_score_gemma":0.01610108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004194836,"about_ca_topic_score_gemma":0.004714231,"domain_scores_codex":[0.9234825,0.03689878,0.02063775,0.002839614,0.01449564,0.001645765],"domain_scores_gemma":[0.7605484,0.06901696,0.01484587,0.02309696,0.1287135,0.003778266],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006599528,0.0001571024,0.001334035,0.005729927,0.00009422214,0.0007832636,0.001510211,0.0005903436,0.004513091,0.01008026,0.9162028,0.05834493],"study_design_scores_gemma":[0.0001363539,0.00007808246,0.002173356,0.004533374,0.00005275409,0.0007011135,0.0004099733,0.0006819888,0.002514377,0.008994336,0.979624,0.0001001237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006170787,0.01397122,0.4506607,0.07589401,0.01340809,0.03442188,0.2516076,0.05134228,0.1025234],"genre_scores_gemma":[0.0108363,0.008636378,0.6518195,0.0341325,0.003301342,0.0389902,0.2054338,0.01484462,0.03200533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8850388,"threshold_uncertainty_score":0.6079801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2332037992654263,"score_gpt":0.5058561635822396,"score_spread":0.2726523643168133,"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."}}