{"id":"W52545667","doi":"","title":"Состояние племенной базы мясного скотоводства и дальнейшее совершенствование герефордского скота в Красноярском крае","year":2014,"lang":"ru","type":"article","venue":"Vestnik Altajskogo gosudarstvennogo agrarnogo universiteta","topic":"Food Industry and Aquatic Biology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Withers; Rump; Biology; Animal science; Breed; Animal breeding; Beef cattle; Veterinary medicine; Body weight; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.002171278,0.002461573,0.002638538,0.0004945846,0.002809382,0.0007478015,0.00432066,0.002933618,0.0136285],"category_scores_gemma":[0.0007612424,0.001556811,0.001576664,0.003847227,0.002246762,0.001533183,0.001630061,0.003061483,0.007105479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005294618,"about_ca_system_score_gemma":0.0003686897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002181854,"about_ca_topic_score_gemma":0.001312584,"domain_scores_codex":[0.9863464,0.002383939,0.002242533,0.003595602,0.001447761,0.003983816],"domain_scores_gemma":[0.9918905,0.002036518,0.001712559,0.001615147,0.0006493884,0.002095839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002814302,0.0065375,0.04616541,0.0009709771,0.003266635,0.001187584,0.00440082,0.0008278638,0.1194707,0.1182568,0.300348,0.3957534],"study_design_scores_gemma":[0.003969054,0.006704999,0.02746334,0.0008185807,0.00102885,0.0003665509,0.005638379,0.002058289,0.002777866,0.005161121,0.9396173,0.004395636],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8986506,0.003658296,0.0003811372,0.0210622,0.005571824,0.002137516,0.00106818,0.001161359,0.0663089],"genre_scores_gemma":[0.9550319,0.001263353,0.0006778653,0.003324294,0.004325851,0.00002487984,0.001145706,0.00006925279,0.03413694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6392693,"threshold_uncertainty_score":0.9992385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419723699030709,"score_gpt":0.1842672392081622,"score_spread":0.1700700022178551,"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."}}