{"id":"W4284890645","doi":"10.1002/bit.28173","title":"On‐line untargeted metabolomics monitoring of an <i>Escherichia coli</i> succinate fermentation process","year":2022,"lang":"en","type":"article","venue":"Biotechnology and Bioengineering","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Directorate for Biological Sciences; Industrial Biotechnology Innovation Centre; McGill University","keywords":"Bioprocess; Fermentation; Process analytical technology; Bioreactor; Metabolomics; Escherichia coli; Mass spectrometry; Chemistry; Partial least squares regression; Pentose; Chromatography; Biochemical engineering; Biochemistry; Biotechnology; Biology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002624522,0.0004965953,0.0003513584,0.0001768389,0.0001147331,0.0004126472,0.0002625418,0.0003615005,0.0005636101],"category_scores_gemma":[0.000241785,0.0001409519,0.0002341602,0.0001641999,0.0002287735,0.0002500725,0.0003086669,0.0004043866,0.0002492095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002680621,"about_ca_system_score_gemma":0.0002473286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006707906,"about_ca_topic_score_gemma":0.0009690634,"domain_scores_codex":[0.9996709,0.00005937964,0.00001386502,0.0001008542,0.000121102,0.00003399457],"domain_scores_gemma":[0.9998472,0.00004252061,0.00004399509,0.00001900932,0.00003252023,0.00001474599],"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":[0.0000507887,0.00001395795,0.0002456474,0.0000124607,0.000002587309,0.00001118983,0.000004319815,0.00004829359,0.9981714,0.00001376156,0.00001482419,0.001410693],"study_design_scores_gemma":[0.000003162648,0.0000735104,0.00235971,0.000001397018,0.000004735484,0.00003328341,0.000006020398,0.001357618,0.9957974,0.000011355,0.0003476382,0.000004224703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588513,0.0005039083,0.03807772,0.0001186963,0.00004245387,0.00005787944,0.0007351178,0.0003955919,0.001217417],"genre_scores_gemma":[0.9645622,0.0006854295,0.03100933,0.0001548786,0.00001815462,0.00007725303,0.0008200955,0.00009791819,0.002574756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006707906,"threshold_uncertainty_score":0.0019449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008750180037402902,"score_gpt":0.2439415155462726,"score_spread":0.2351913355088697,"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."}}