{"id":"W2987986715","doi":"10.35694/yarcx.2019.47.3.003","title":"Скрининг коллекционных образцов сои по скороспелости и продуктивности в условиях Рязанской области","year":2019,"lang":"ru","type":"article","venue":"Vestnik APK Verhnevolzh`ia","topic":"Agricultural Productivity and Crop Improvement","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Resistance (ecology); Productivity; Selection (genetic algorithm); New Variety; Agricultural science; Biology; Geography; Cultivar; Horticulture; Agronomy; Biotechnology; Economic growth; 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":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001258171,0.001820575,0.001758537,0.0001293581,0.001368699,0.0009410862,0.002309483,0.001205473,0.01529673],"category_scores_gemma":[0.000275236,0.0008810336,0.001042627,0.002086448,0.0006900033,0.00190238,0.00128215,0.001680967,0.01856841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004970147,"about_ca_system_score_gemma":0.0001738269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002622389,"about_ca_topic_score_gemma":0.0009789453,"domain_scores_codex":[0.9895948,0.0005780166,0.001691752,0.003254318,0.001970469,0.002910702],"domain_scores_gemma":[0.9957924,0.000464318,0.0009942284,0.001109688,0.0005278658,0.001111476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009533106,0.003630426,0.0358266,0.0007257833,0.0008888723,0.0001832583,0.001391696,0.000244505,0.6759287,0.01012486,0.1360658,0.1340362],"study_design_scores_gemma":[0.002716268,0.00561498,0.3227382,0.0006694462,0.0005309504,0.0001715866,0.004515649,0.000427943,0.02809362,0.002737818,0.6270375,0.004746049],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9523239,0.003831683,0.00001015756,0.01345368,0.005734223,0.002736088,0.0003642831,0.0005643841,0.02098166],"genre_scores_gemma":[0.9271604,0.001118778,0.0002224094,0.00215887,0.003690392,0.0001094485,0.0004626156,0.00003685525,0.06504023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6478351,"threshold_uncertainty_score":0.9999314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197683988402508,"score_gpt":0.2206591387332128,"score_spread":0.2086822988491877,"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."}}