{"id":"W4210389004","doi":"10.3390/metabo12020148","title":"Fecal 1H-NMR Metabolomics: A Comparison of Sample Preparation Methods for NMR and Novel in Silico Baseline Correction","year":2022,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Lethbridge","funders":"Agriculture and Agri-Food Canada; Canadian Institutes of Health Research; Alberta Agriculture and Forestry; University of Lethbridge; Natural Sciences and Engineering Research Council of Canada; Canadian Poultry Research Council","keywords":"Metabolome; Metabolomics; Feces; Chromatography; Extraction (chemistry); In silico; Chemistry; Ultrafiltration (renal); Biology; Biochemistry; Microbiology","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":[0.002393749,0.00126186,0.0007120459,0.0007031862,0.0004007988,0.0007494418,0.0008716933,0.0008628889,0.001733065],"category_scores_gemma":[0.003396754,0.0005938098,0.000518038,0.0006962944,0.0004757591,0.0007175817,0.0007002351,0.0009282305,0.0008096066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003804483,"about_ca_system_score_gemma":0.000598255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009880555,"about_ca_topic_score_gemma":0.002919119,"domain_scores_codex":[0.9989614,0.000292468,0.00006210682,0.0002721052,0.0003475208,0.0000643415],"domain_scores_gemma":[0.9982468,0.0005980407,0.0003174857,0.0002594797,0.0005016525,0.00007649064],"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":[0.0007105732,0.000134652,0.0009800954,0.000437672,0.00006710868,0.00005810465,0.00006475913,0.001117326,0.9691821,0.0001957364,0.0001884076,0.0268636],"study_design_scores_gemma":[0.0001229554,0.001555819,0.00877429,0.00006252182,0.0002226958,0.0006503805,0.00009159112,0.02823628,0.95162,0.00042104,0.008122926,0.0001194425],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2416611,0.002857605,0.7490918,0.0004120071,0.0003155081,0.0007825451,0.001103142,0.00190542,0.001870941],"genre_scores_gemma":[0.1748203,0.002708954,0.8168369,0.0002940405,0.0001112527,0.0006701971,0.001637315,0.0006714763,0.002249472],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002393749,"threshold_uncertainty_score":0.01265955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02665771548304463,"score_gpt":0.3626138958103954,"score_spread":0.3359561803273508,"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."}}