{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002219127,0.0008225659,0.0004996722,0.002349016,0.005826016,0.01134197,0.001037824,0.00229503,0.05845153],"category_scores_gemma":[0.004614016,0.0007492975,0.0007555028,0.002573185,0.005823503,0.005142206,0.003599948,0.003962654,0.02231341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006342797,"about_ca_system_score_gemma":0.01105627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01616521,"about_ca_topic_score_gemma":0.0230625,"domain_scores_codex":[0.9963011,0.0008383596,0.0001887933,0.0006479459,0.001463644,0.0005600514],"domain_scores_gemma":[0.9977092,0.0005260415,0.0002310602,0.0003689607,0.0008169218,0.0003477464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001063449,0.00006519856,0.002595685,0.0003728164,0.00003276683,0.0004859125,0.006791275,0.0005899245,0.002062651,0.8522333,0.03515881,0.0995053],"study_design_scores_gemma":[0.00001771372,0.00003302502,0.003782515,0.0003055166,0.0000266569,0.0003341799,0.003305679,0.0002809636,0.001469549,0.07393698,0.9164558,0.00005141821],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0231549,0.008774048,0.0215929,0.01251066,0.001339207,0.0001623453,0.0007604241,0.0003377057,0.9313678],"genre_scores_gemma":[0.4256487,0.01317912,0.03481208,0.002310401,0.0006532116,0.0004916425,0.001288758,0.0007997944,0.5208163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05845153,"threshold_uncertainty_score":0.1955398,"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."}}