{"id":"W1603442075","doi":"","title":"栽培稻(Oryza sativa L.)亚种间F1花粉不育基因S-a的精细定位及克隆","year":2003,"lang":"ja","type":"article","venue":"分子植物育种","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Oryza sativa; Biology; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001187323,0.0005539627,0.0004851827,0.000165754,0.0002917412,0.0001753144,0.0007344969,0.0008700183,0.001069068],"category_scores_gemma":[0.001418098,0.0005081816,0.0003590513,0.0003461407,0.0007051276,0.0000116874,0.0003554156,0.0005305043,0.001419005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007072232,"about_ca_system_score_gemma":0.0006449823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005144842,"about_ca_topic_score_gemma":0.00006277589,"domain_scores_codex":[0.995788,0.000387129,0.0008362466,0.0007663975,0.0008696376,0.001352561],"domain_scores_gemma":[0.9974192,0.00007391158,0.0002210281,0.001143692,0.0003331261,0.0008090336],"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":[0.0003551823,0.002044763,0.009531475,0.001685564,0.0013411,0.0001504921,0.002398429,0.00003491877,0.4831413,0.003105065,0.3112164,0.1849953],"study_design_scores_gemma":[0.001732399,0.0009925929,0.002052135,0.0000899663,0.00007257053,0.00005283102,0.001057565,0.000126646,0.1598344,0.0005260796,0.8326225,0.000840301],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.354094,0.02629709,0.005493561,0.003569854,0.005496895,0.002199054,0.0004779572,0.0001250446,0.6022465],"genre_scores_gemma":[0.9676998,0.004589444,0.002482285,0.001009498,0.0009684652,0.00002889261,0.0002356907,0.00007764725,0.02290828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6136057,"threshold_uncertainty_score":0.9998441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01845912007724241,"score_gpt":0.2847141331510981,"score_spread":0.2662550130738556,"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."}}