{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007716964,0.0004342911,0.0003370542,0.001112833,0.00246751,0.004103963,0.0008430234,0.0007127329,0.01259562],"category_scores_gemma":[0.001237478,0.0003026567,0.0004037008,0.001697595,0.002585547,0.00379024,0.001071824,0.001326783,0.002689067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003998008,"about_ca_system_score_gemma":0.004970476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01947894,"about_ca_topic_score_gemma":0.008083622,"domain_scores_codex":[0.9993773,0.00009564694,0.00002871563,0.0001591008,0.0002216856,0.000117559],"domain_scores_gemma":[0.9995546,0.00006720489,0.00006005714,0.00002539879,0.0002133375,0.00007946542],"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":[0.0004132242,0.0001048988,0.007383148,0.0005806331,0.0001147113,0.000898786,0.01026737,0.002860739,0.02255923,0.7310492,0.03188772,0.1918803],"study_design_scores_gemma":[0.0001302741,0.0002477193,0.01534069,0.0003064869,0.0001644398,0.0009719131,0.02708162,0.003276802,0.04555708,0.2429739,0.6637459,0.0002031672],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2612656,0.01378271,0.04257883,0.01204195,0.001270954,0.0003492995,0.0010538,0.000687001,0.6669698],"genre_scores_gemma":[0.8435761,0.006684481,0.01947218,0.000938013,0.0001509062,0.0002686412,0.0005255206,0.00009969496,0.1282844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01947894,"threshold_uncertainty_score":0.04213655,"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."}}