{"id":"W3177912108","doi":"","title":"Изучение и подбор исходного материала сои для создания новых сортов","year":2018,"lang":"ru","type":"article","venue":"Аграрная наука","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ripeness; Geography; Horticulture; Productivity; Biology; Forestry; Agronomy; Agricultural science; Ripening","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":[0.0007839712,0.0002851428,0.0002834115,0.001833091,0.001045023,0.001628407,0.0003971116,0.0006102357,0.01003601],"category_scores_gemma":[0.001716393,0.0005221372,0.0003840705,0.001838475,0.001156127,0.0008120436,0.0006704575,0.0009729948,0.003446384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007380223,"about_ca_system_score_gemma":0.00148949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003507264,"about_ca_topic_score_gemma":0.008751198,"domain_scores_codex":[0.9990762,0.0001341947,0.00006110387,0.0001820274,0.0004650663,0.00008138668],"domain_scores_gemma":[0.9991646,0.0002160619,0.0001954453,0.0001560594,0.0002074993,0.00006033032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002510418,0.0001167764,0.009789041,0.0008501444,0.00006215866,0.002029385,0.003254573,0.001213534,0.3927005,0.05227261,0.002994352,0.5344658],"study_design_scores_gemma":[0.00008312899,0.0004277933,0.0491793,0.0003885056,0.0002209049,0.005789831,0.003212044,0.002345499,0.1994003,0.0256675,0.7130433,0.0002419429],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5325521,0.05609336,0.1808484,0.002856915,0.001514051,0.0005099007,0.001643555,0.0008049962,0.2231766],"genre_scores_gemma":[0.8660695,0.0141631,0.09117109,0.0001460725,0.0002235699,0.0003028272,0.0003351972,0.0002035936,0.02738519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01003601,"threshold_uncertainty_score":0.03357387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02928146620362743,"score_gpt":0.2321664377568154,"score_spread":0.2028849715531879,"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."}}