{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003549938,0.0001837446,0.0003222038,0.0005603076,0.0002326449,0.0001068508,0.001885866,0.0001601764,0.0005068525],"category_scores_gemma":[0.001518246,0.0001183019,0.0001076376,0.000591837,0.0002080223,0.000690112,0.0004963668,0.0008792768,0.00003763309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003357772,"about_ca_system_score_gemma":0.0003640015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000226407,"about_ca_topic_score_gemma":0.000002699212,"domain_scores_codex":[0.9968789,0.0001066922,0.001003143,0.0004165767,0.001088039,0.0005066515],"domain_scores_gemma":[0.9963477,0.0001524205,0.0005792861,0.001204242,0.001569656,0.0001467524],"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":[0.00009551772,0.00007687574,0.0001729844,0.0001112888,0.00004119287,0.00001918634,0.00002932291,0.000001145942,0.9879068,0.0005759257,0.004385091,0.006584709],"study_design_scores_gemma":[0.0008763628,0.0003683111,0.00005264134,0.001003419,0.00001821517,0.0001438896,0.0001489788,0.000263115,0.9250837,0.04647587,0.02528766,0.0002778517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2349405,0.0007581682,0.7361093,0.01328547,0.00009517172,0.003139179,0.0002825572,0.0002283496,0.01116126],"genre_scores_gemma":[0.4261097,0.0004619034,0.5652008,0.00002653942,0.001480669,0.0004021446,0.00001992446,0.00009245877,0.00620583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1911692,"threshold_uncertainty_score":0.5549679,"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."}}