{"id":"W4390698897","doi":"10.3389/fphys.2023.1229152","title":"Utilizing NMR fecal metabolomics as a novel technique for detecting the physiological effects of food shortages in waterfowl","year":2024,"lang":"en","type":"article","venue":"Frontiers in Physiology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Environment and Climate Change Canada","keywords":"Economic shortage; Waterfowl; Metabolomics; Feces; Food shortage; Food science; Biology; Bioinformatics; Microbiology; Ecology","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":[0.0001786794,0.0004835256,0.0002640455,0.0005509721,0.0002971925,0.000354486,0.0001085804,0.0003419466,0.0007848301],"category_scores_gemma":[0.0001726738,0.0001367058,0.0001252563,0.0002911901,0.0002766225,0.0002702781,0.0002039603,0.0003241446,0.0001111172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001765361,"about_ca_system_score_gemma":0.0002331663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001823473,"about_ca_topic_score_gemma":0.00567314,"domain_scores_codex":[0.9999269,0.00001437523,0.000003403518,0.00002796329,0.0000152731,0.0000120289],"domain_scores_gemma":[0.9998851,0.00001927945,0.00004264967,0.000006358489,0.00002438476,0.0000221873],"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":[0.0002460954,0.00003618987,0.01275387,0.0000969676,0.0000329108,0.00007007145,0.00003311902,0.00006795328,0.9799806,0.00003757439,0.00006975462,0.006574783],"study_design_scores_gemma":[0.0000320629,0.002354821,0.6076463,0.00004309264,0.0001813451,0.0008367755,0.0004208641,0.002533313,0.3824071,0.0003179357,0.00318197,0.00004434515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802796,0.002712138,0.01388583,0.0001788629,0.00005644118,0.00006225523,0.00109597,0.0000956166,0.00163336],"genre_scores_gemma":[0.9671048,0.002757447,0.02679571,0.0003349466,0.00007069398,0.0001011904,0.0008882606,0.00002717302,0.001919825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001823473,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01280462136929065,"score_gpt":0.2598017881152722,"score_spread":0.2469971667459816,"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."}}