{"id":"W3165074360","doi":"10.15252/msb.20209536","title":"From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions","year":2021,"lang":"en","type":"article","venue":"Molecular Systems Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; European Research Council; National Institutes of Health; SystemsX.ch; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; European Commission; National Research Foundation of Korea; Howard Hughes Medical Institute","keywords":"Biology; Proteome; Escherichia coli; Computational biology; Microbiology; Biochemistry; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006261806,0.000163015,0.0002121279,0.00002123287,0.0001824578,0.00003845999,0.000297275,0.0001828515,0.0002210376],"category_scores_gemma":[0.00005408046,0.0001349563,0.000085391,0.0001914353,0.0000933925,0.0000225848,0.000200436,0.0002050144,0.0001097048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000633599,"about_ca_system_score_gemma":0.00006592249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005562148,"about_ca_topic_score_gemma":0.00003339062,"domain_scores_codex":[0.9989555,0.00006798904,0.0002508546,0.0003993404,0.00007611118,0.0002502135],"domain_scores_gemma":[0.9989759,0.00005662223,0.0001063492,0.0006242768,0.0001460462,0.00009084385],"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":[0.000005353926,0.00003131412,0.00005259572,0.00001541117,0.00005643989,0.00002116375,0.00004262567,0.0001280849,0.9653965,0.03314744,0.001067586,0.0000354372],"study_design_scores_gemma":[0.0004104458,0.00003212128,0.00009886724,0.00006398234,0.00006086868,0.00002896134,0.0004786346,0.0004572512,0.9303361,0.01819021,0.04944064,0.0004019767],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7311111,0.0003460373,0.2616076,0.002534556,0.0001420535,0.0006347836,0.0007406534,0.0001882756,0.002694919],"genre_scores_gemma":[0.9877687,0.0000137119,0.008421431,0.0007094457,0.0001610633,0.001442136,0.0004561446,0.00003103861,0.0009963029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2566576,"threshold_uncertainty_score":0.5503358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01316984858442262,"score_gpt":0.2740495418554759,"score_spread":0.2608796932710533,"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."}}