{"id":"W4280617701","doi":"10.1101/2022.04.25.489416","title":"Method for quantifying the metabolic boundary fluxes of cell cultures in large cohorts by high resolution hydrophilic liquid chromatography mass spectrometry","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; MD Precision (Canada); University of Calgary","funders":"Genome Alberta; University of Calgary; Alberta Innovates; International Microbiome Centre, University of Calgary; Alberta Precision Laboratories; Genome Canada; Government of Alberta","keywords":"Metabolomics; Mass spectrometry; Chromatography; Hydrophilic interaction chromatography; Leverage (statistics); Chemistry; Liquid chromatography–mass spectrometry; Biological system; High-performance liquid chromatography; Computer science; Biology; Artificial intelligence","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":[0.001730068,0.0009370159,0.0006301059,0.00133703,0.0006247891,0.001078248,0.0007050191,0.001009109,0.0009613878],"category_scores_gemma":[0.002500348,0.0003908805,0.0004589949,0.0009692186,0.0006101078,0.0005458325,0.0009603291,0.001284233,0.0008479785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004754766,"about_ca_system_score_gemma":0.0007836605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009957255,"about_ca_topic_score_gemma":0.001935483,"domain_scores_codex":[0.9982026,0.0003325247,0.00009632155,0.0005754604,0.0007192748,0.00007380442],"domain_scores_gemma":[0.9989065,0.0003640012,0.0002249366,0.0002084786,0.0002340827,0.00006201221],"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":[0.00008046622,0.00009357402,0.00395001,0.00007531672,0.00005587756,0.00003503849,0.00004841023,0.0006018288,0.9825459,0.0004149133,0.0003606257,0.01173807],"study_design_scores_gemma":[0.00003677283,0.0003368685,0.02449014,0.0000278359,0.00007378871,0.0002284866,0.00009505302,0.02612738,0.9420672,0.001273716,0.005165477,0.00007735661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1564462,0.001067736,0.8348512,0.0004273832,0.0001807034,0.0006432859,0.003135565,0.001588166,0.001659801],"genre_scores_gemma":[0.3366289,0.00102568,0.6545805,0.00056876,0.00009624121,0.002021782,0.002731454,0.0002769188,0.002069831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001730068,"threshold_uncertainty_score":0.009149611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006078634856817,"score_gpt":0.2528408311935322,"score_spread":0.2427800448449641,"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."}}