{"id":"W2155896892","doi":"10.5376/mpb.cn.2010.01.0002","title":"利用分子标记辅助选择培育和评价三种Bt基因(cry1Ac, cry1C*和cry2A*)抗虫水稻","year":2010,"lang":"ja","type":"article","venue":"分子植物育种","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cry1Ac; Mathematics; Chemistry; Genetically modified crops","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001375998,0.0003122131,0.0001536522,0.0007826803,0.001513833,0.004793795,0.0004209906,0.0008099029,0.006825319],"category_scores_gemma":[0.001737534,0.0002266447,0.0001834386,0.0008049135,0.003956298,0.003673004,0.0007308692,0.001024359,0.0009878823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004438242,"about_ca_system_score_gemma":0.003492764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01112524,"about_ca_topic_score_gemma":0.009028497,"domain_scores_codex":[0.9993407,0.0002024735,0.0000263153,0.0001212804,0.0002295509,0.00007965727],"domain_scores_gemma":[0.9991621,0.0002971552,0.0001147074,0.00003056702,0.00031877,0.00007682408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000647115,0.00008228482,0.006108962,0.0002283628,0.00004649274,0.0002327623,0.01138725,0.003323517,0.004784829,0.8834469,0.006549079,0.08374483],"study_design_scores_gemma":[0.00004502525,0.0001655446,0.02880287,0.0003346823,0.00008901185,0.0003744167,0.03958321,0.004743973,0.0106954,0.6245894,0.2904738,0.0001027481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2084729,0.003812915,0.03381202,0.02096073,0.0002992749,0.0001101642,0.0001762871,0.00009257359,0.7322631],"genre_scores_gemma":[0.9222849,0.002256432,0.01261362,0.001002628,0.00008599221,0.00007887245,0.00006588054,0.0000291235,0.06158263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01112524,"threshold_uncertainty_score":0.03220183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006255359219236152,"score_gpt":0.2018245767621515,"score_spread":0.1955692175429154,"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."}}