{"id":"W2523266736","doi":"10.1111/asj.12683","title":"Leather quality of beefalo‐Nellore cattle in different production systems","year":2016,"lang":"en","type":"article","venue":"Animal Science Journal","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul","keywords":"Production system (computer science); Silage; Pasture; Animal science; Brachiaria; Carcass weight; Biology; Body weight; Beef cattle; Significant difference; Agronomy; Forage; Medicine; Production (economics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002746736,0.0001067556,0.0002181067,0.00003028244,0.0002149375,0.00006695223,0.000381849,0.00004159476,0.000175976],"category_scores_gemma":[0.0003695816,0.0000285723,0.00007001616,0.0004663354,0.0003504084,0.0004492742,0.00006318636,0.0001177549,0.00001536581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008230857,"about_ca_system_score_gemma":0.0000280468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002970045,"about_ca_topic_score_gemma":0.0002023206,"domain_scores_codex":[0.9981991,0.0001360719,0.0004515794,0.0003098847,0.0005647541,0.0003385771],"domain_scores_gemma":[0.9993168,0.00007008824,0.0002436001,0.00006243501,0.0001792299,0.0001279138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000489179,0.00008567042,0.1211744,0.000004813718,0.000001508967,6.525395e-7,0.00007444925,7.440524e-7,0.8699763,0.0005610537,0.0001000352,0.007971443],"study_design_scores_gemma":[0.00009140252,0.000278252,0.9384421,0.00006103134,0.000002011833,0.00002064596,0.0004044101,0.000003065263,0.05977589,0.0004833507,0.0003351865,0.0001027026],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962018,0.0001014145,0.000002977284,0.002818735,0.0003210142,0.0001050209,0.000004833581,0.00001173608,0.0004325083],"genre_scores_gemma":[0.9990498,0.0000431263,0.00001250266,0.0000169177,0.0005931647,0.000002160059,3.242966e-7,5.707954e-7,0.0002813644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8172677,"threshold_uncertainty_score":0.1926813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08468376078082268,"score_gpt":0.3002148345776642,"score_spread":0.2155310737968415,"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."}}