{"id":"W4367854237","doi":"10.1093/tas/txad043","title":"Predicting fecal composition, intake, and nutrient digestibility in beef cattle consuming high forage diets using near infrared spectroscopy","year":2023,"lang":"en","type":"article","venue":"Translational Animal Science","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"Alberta Beef Producers","keywords":"Feces; Forage; Composition (language); Nutrient; Food science; Animal science; Chemistry; Agronomy; Biology; 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":[0.0007872112,0.0003084448,0.0002691774,0.000355108,0.0001642918,0.0003702498,0.000146026,0.0002392793,0.0002409429],"category_scores_gemma":[0.0006964101,0.0001411691,0.0001872553,0.0002956477,0.0001131219,0.0002056639,0.0001329908,0.0001621427,0.00009017078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002546891,"about_ca_system_score_gemma":0.0001669757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006016919,"about_ca_topic_score_gemma":0.01183525,"domain_scores_codex":[0.9998062,0.00006368009,0.000008121247,0.00004186774,0.00006484603,0.00001531169],"domain_scores_gemma":[0.9997134,0.0001249863,0.00007126812,0.00001380847,0.00005726969,0.0000191791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005378161,0.0002479835,0.7911265,0.00006493097,0.0001937187,0.0000517822,0.00009363596,0.01002523,0.1524781,0.00003316293,0.000106792,0.04504033],"study_design_scores_gemma":[0.00001250178,0.0004974516,0.9085528,0.00001170795,0.00007025814,0.0001728493,0.0001336741,0.06800621,0.0222165,0.00007332067,0.0002376537,0.0000151019],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952816,0.00009415158,0.004446541,0.000004982006,8.510966e-7,0.000003515895,0.00005835978,0.00001847763,0.00009143996],"genre_scores_gemma":[0.993427,0.00006546935,0.006099175,0.00001100422,0.000001626603,0.00000466571,0.0002099249,0.000003727691,0.0001772827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006016919,"threshold_uncertainty_score":0.01196378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03421002245588898,"score_gpt":0.2753990412333197,"score_spread":0.2411890187774307,"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."}}