{"id":"W779994955","doi":"","title":"利用分子标记辅助选择聚合Pi9（t）和Xa23基因","year":2007,"lang":"zh","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":"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":[],"category_scores_codex":[0.0006133305,0.0003014615,0.0002857056,0.0002021115,0.0001373129,0.00002469985,0.0003399736,0.0005566157,0.0006892232],"category_scores_gemma":[0.00004808019,0.0003281613,0.000119878,0.0003486544,0.0001748479,0.0001620431,0.00006237614,0.0006383899,0.001277085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006987687,"about_ca_system_score_gemma":0.0000303479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007453922,"about_ca_topic_score_gemma":0.0002629756,"domain_scores_codex":[0.9983706,0.00002313764,0.0003938828,0.0003067721,0.000168304,0.0007373411],"domain_scores_gemma":[0.9991492,0.0001089923,0.00003344243,0.0005434548,0.00003607022,0.0001288364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002028733,0.00006416257,0.002808614,0.000170534,0.0001465057,0.0002908589,0.0007012596,0.0003837459,0.001854997,0.9544537,0.005169854,0.03393548],"study_design_scores_gemma":[0.001850365,0.0004580479,0.1275653,0.000302771,0.0002143298,0.0002198979,0.004858669,0.002145304,0.004177014,0.78694,0.06903429,0.002233925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03471832,0.01045597,0.01017036,0.0003029436,0.00141026,0.0001536024,0.0000167213,0.0009100301,0.9418618],"genre_scores_gemma":[0.9943144,0.0003871054,0.001816132,0.00007130121,0.0004047524,0.000004305195,0.00001064847,0.00004563008,0.002945703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9595961,"threshold_uncertainty_score":0.999917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008545006096791184,"score_gpt":0.2148030334264844,"score_spread":0.2062580273296932,"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."}}