{"id":"W6907777619","doi":"10.24412/2309-348x-2022-4-49-57","title":"КОНКУРЕНТОСПОСОБНОСТЬ ОТЕЧЕСТВЕННОГО СОРТА СОИ ЭН АРГЕНТА ПО СРАВНЕНИЮ С СОРТОМ КАНАДСКОЙ СЕЛЕКЦИИ ОАК ПРУДЕНС В УСЛОВИЯХ ЦЕНТРАЛЬНОГО ЧЕРНОЗЕМЬЯ","year":2022,"lang":"ru","type":"article","venue":"CyberLeninK (CyberLeninka)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":[0.002402435,0.0005330485,0.0003680137,0.00208102,0.003484024,0.00885874,0.0009205017,0.001801906,0.02770792],"category_scores_gemma":[0.006654111,0.0005588207,0.0005682329,0.00219199,0.006014039,0.004835686,0.002813444,0.002404384,0.00828818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004320165,"about_ca_system_score_gemma":0.008900251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009137368,"about_ca_topic_score_gemma":0.01165018,"domain_scores_codex":[0.9958277,0.001063759,0.0001952791,0.0007109623,0.001772094,0.000430123],"domain_scores_gemma":[0.9961617,0.001122283,0.000427829,0.0005080964,0.001359541,0.0004204621],"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":[0.00007590288,0.000061847,0.003419769,0.0003327186,0.0000305172,0.00049347,0.007101873,0.0007821408,0.003132924,0.8673643,0.01760595,0.0995985],"study_design_scores_gemma":[0.00003421351,0.0000592394,0.007097149,0.0003931532,0.00004702446,0.000873303,0.008053225,0.001089447,0.004381768,0.2581325,0.7197438,0.00009534263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04823606,0.009028532,0.06014254,0.01494395,0.001066961,0.0002019407,0.0006710055,0.0003442279,0.8653647],"genre_scores_gemma":[0.738172,0.01041068,0.06706679,0.001827471,0.0006317729,0.0005100023,0.000591617,0.0004364545,0.1803533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9908626,"threshold_uncertainty_score":0.0926922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009578853007724824,"score_gpt":0.2025372887995132,"score_spread":0.1929584357917884,"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."}}