{"id":"W2154839478","doi":"10.5539/ass.v11n8p201","title":"Increasing Efficiency of Breeding Dairy Cattle in Agricultural Organizations of the Russian Federation","year":2015,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Agricultural Development and Policies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Russian federation; Agriculture; Production (economics); Milk production; Business; Work (physics); State (computer science); Agricultural economics; Agricultural science; Agricultural productivity; Economics; Geography; Economic policy; Environmental science; Computer science; Engineering; Animal science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004113649,0.00007514461,0.0001125047,0.00001618568,0.000456584,0.00005030831,0.0003813069,0.00004688531,0.00001426018],"category_scores_gemma":[0.0002237514,0.00002366622,0.00003155546,0.002819816,0.0003488845,0.0002764147,0.0001174682,0.00006341017,0.000002328416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007536448,"about_ca_system_score_gemma":0.00007087945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000890742,"about_ca_topic_score_gemma":0.0007390068,"domain_scores_codex":[0.9990061,0.00005723601,0.000215358,0.0001358985,0.00038495,0.0002004909],"domain_scores_gemma":[0.9995847,0.0000353719,0.0001453381,0.00002514994,0.000155487,0.00005390274],"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":[0.000008654587,0.0001105308,0.1688908,0.000006815674,0.000003683436,2.352094e-7,0.02104151,0.000008625912,0.7588176,0.03464402,0.0004092166,0.01605839],"study_design_scores_gemma":[0.00005526529,0.00002617297,0.9807169,0.00001585842,0.000002497561,0.000002774941,0.006358871,0.00000365402,0.01233407,0.0003129711,0.00009822842,0.00007278319],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470295,0.00001134024,0.000001598829,0.002665096,0.0001231934,0.0001129578,0.000005007165,0.00001258398,0.05003868],"genre_scores_gemma":[0.9997166,0.00000111749,0.00004930878,0.00003143166,0.0001022007,0.000001830727,0.000006561504,3.573024e-7,0.00009059125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8118261,"threshold_uncertainty_score":0.3511721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714500157817572,"score_gpt":0.2279363478087526,"score_spread":0.2107913462305769,"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."}}