{"id":"W2967619458","doi":"10.1101/733576","title":"Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; National Eye Institute; National Human Genome Research Institute; National Institute of General Medical Sciences; National Institute of Mental Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Science Foundation; National Institutes of Health; Agence Nationale de la Recherche","keywords":"Human proteome project; Proteome; Workflow; Pipeline (software); Identifier; Computer science; UniProt; Data science; Computational biology; Information retrieval; Proteomics; Bioinformatics; Chemistry; Biology; Database; Biochemistry","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":[0.037559,0.002058388,0.001816907,0.01007656,0.002665519,0.007329585,0.007047189,0.004180495,0.06978248],"category_scores_gemma":[0.06354551,0.002126447,0.002317663,0.006397144,0.001716701,0.004620099,0.004617486,0.006074366,0.09081241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002997874,"about_ca_system_score_gemma":0.01153693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006913509,"about_ca_topic_score_gemma":0.005750503,"domain_scores_codex":[0.9798056,0.00705717,0.004197014,0.001770174,0.006307873,0.0008620779],"domain_scores_gemma":[0.9387882,0.01372109,0.003950721,0.007811782,0.03376139,0.001966822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003806159,0.00004702721,0.0008978596,0.002038026,0.00004988335,0.0003233301,0.000310684,0.0002896087,0.005210494,0.00491108,0.9542499,0.03129141],"study_design_scores_gemma":[0.00009761899,0.00004999937,0.002163724,0.001637875,0.00003326632,0.000417741,0.0001694751,0.0004290002,0.00356033,0.006176555,0.9852004,0.00006409402],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.005295281,0.008077702,0.248813,0.03229884,0.008394235,0.009093782,0.5074403,0.08062023,0.09996662],"genre_scores_gemma":[0.0109642,0.005960667,0.4085502,0.02539804,0.001438228,0.01167344,0.4838673,0.01915994,0.03298793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06978248,"threshold_uncertainty_score":0.2334456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04344553737366887,"score_gpt":0.3176900390074763,"score_spread":0.2742445016338074,"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."}}