{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001044836,0.001153473,0.001361515,0.0001340527,0.001665731,0.0006242025,0.001375124,0.001109711,0.0324889],"category_scores_gemma":[0.000181179,0.0005639038,0.0008619425,0.001643607,0.0004665134,0.0007090114,0.0002428884,0.001209069,0.00688645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002829582,"about_ca_system_score_gemma":0.0001567609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137442,"about_ca_topic_score_gemma":0.001375846,"domain_scores_codex":[0.9922044,0.0004708363,0.001721041,0.001854863,0.001275974,0.002472918],"domain_scores_gemma":[0.9964755,0.0003798492,0.0005937525,0.0004505556,0.0004591488,0.001641183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009332231,0.004208111,0.00790689,0.0002156854,0.0001276753,0.0002209908,0.0006790407,0.00000444687,0.02296003,0.1104375,0.6474393,0.2048671],"study_design_scores_gemma":[0.002025899,0.00608083,0.2968868,0.0003602404,0.0001448382,0.0001396051,0.003328593,0.0003373354,0.001098624,0.01412313,0.6732624,0.002211691],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5991223,0.009288645,0.00009181917,0.172481,0.00214643,0.002281551,0.0004498627,0.001401805,0.2127366],"genre_scores_gemma":[0.9109707,0.007863511,0.000633626,0.03005275,0.003578298,0.00002856582,0.0002682436,0.00001466679,0.04658957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3118485,"threshold_uncertainty_score":0.9996812,"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."}}