{"id":"W4323656789","doi":"10.35172/rvz.2023.v30.1052","title":"BIOMASS SORGHUM SILAGES WITH SUGARCANE","year":2023,"lang":"en","type":"article","venue":"Veterinária e Zootecnia","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Universidade Federal de Lavras; Universidade Federal de Viçosa","keywords":"Silage; Sorghum; Dry matter; Biomass (ecology); Agronomy; Neutral Detergent Fiber; Forage; Hemicellulose; Sweet sorghum; Fermentation; Lignin; Completely randomized design; Organic matter; Biology; Animal science; Food science; Botany","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002349241,0.0007357909,0.0004861263,0.0004222337,0.0003025375,0.000561372,0.000263355,0.000195448,0.002263047],"category_scores_gemma":[0.0001893603,0.0002117648,0.0003245714,0.0005202874,0.0002120994,0.0002645058,0.0004278027,0.0004590527,0.0003967406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004366242,"about_ca_system_score_gemma":0.0004071316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00282402,"about_ca_topic_score_gemma":0.004182747,"domain_scores_codex":[0.9997446,0.00004011623,0.00003334066,0.00006000798,0.00007899779,0.00004291756],"domain_scores_gemma":[0.9996526,0.00004405958,0.0001131404,0.00002639848,0.00006213367,0.0001016992],"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":[0.001646109,0.000178737,0.0006405943,0.00009310078,0.00002690039,0.00003311487,0.00004556658,0.00006718651,0.9952003,0.0000284321,0.00001875469,0.002021222],"study_design_scores_gemma":[0.000156212,0.01254819,0.05616369,0.00008592656,0.0002155524,0.0001753231,0.0004140935,0.000873312,0.9219455,0.0001269571,0.007256581,0.00003868134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981778,0.0003657711,0.0005100376,0.00001326351,0.00002095417,0.00003532064,0.0001979211,0.00002473893,0.0006542398],"genre_scores_gemma":[0.9905615,0.0004407181,0.003975872,0.00008784571,0.00001434237,0.0001078734,0.001123295,0.00003362613,0.003655006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00282402,"threshold_uncertainty_score":0.007570624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675398825461465,"score_gpt":0.2276196324562252,"score_spread":0.2008656442016106,"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."}}