{"id":"W3082588488","doi":"","title":"ГЕНЕТИЧЕСКИЙ ПРОГРЕСС ПО ХОЗЯЙСТВЕННО-ПОЛЕЗНЫМ ПРИЗНАКАМ ПРИ СОВЕРШЕНСТВОВАНИИ ЛЕНИНГРАДСКОГО ТИПА ЧЕРНО-ПЕСТРОГО СКОТА","year":2009,"lang":"ru","type":"article","venue":"","topic":"Animal Nutrition and Health","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Breed; Production (economics); Animal science; Biology; Computer science; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007241719,0.0002502273,0.0002689496,0.001339547,0.0009296909,0.001861328,0.0003093026,0.0005578293,0.01517249],"category_scores_gemma":[0.001027871,0.0003506613,0.0002732966,0.001232794,0.001011186,0.0007850931,0.0007488782,0.0009653677,0.007507756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009623033,"about_ca_system_score_gemma":0.00145568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002785388,"about_ca_topic_score_gemma":0.004711148,"domain_scores_codex":[0.999384,0.00009014321,0.00003720816,0.000119574,0.0002884071,0.0000805634],"domain_scores_gemma":[0.9993498,0.0001098343,0.0001298815,0.00008978391,0.0002454168,0.0000753233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003760332,0.0001273282,0.00892174,0.0006993426,0.00005661826,0.00200104,0.003265574,0.001276181,0.180362,0.1763396,0.00926927,0.6173053],"study_design_scores_gemma":[0.00006056813,0.0003422221,0.02064892,0.0002163023,0.000112478,0.0037184,0.002120548,0.001029264,0.0894729,0.03336224,0.8487867,0.0001293771],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3302439,0.03638629,0.1370469,0.004455118,0.001500895,0.0002981805,0.001315688,0.0009457492,0.4878074],"genre_scores_gemma":[0.8124269,0.01206105,0.08400938,0.0002634385,0.0002819106,0.000233331,0.0005244858,0.0002847408,0.08991479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01517249,"threshold_uncertainty_score":0.05075699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03970113715242698,"score_gpt":0.2725567851263779,"score_spread":0.2328556479739509,"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."}}