{"id":"W4233454966","doi":"10.5376/mpb.cn.2012.10.0062","title":"过量表达拟南芥NPR1基因提高小麦纹枯病的抗性","year":2012,"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":false,"ca_institutions":"","funders":"","keywords":"Computer science","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":[0.0004835443,0.0002578407,0.0002609312,0.0002625723,0.0009124798,0.001396603,0.0007920949,0.000634682,0.01097381],"category_scores_gemma":[0.001010914,0.0001511953,0.0003244414,0.0002760526,0.0007619457,0.001623105,0.0005283859,0.001167903,0.003262583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479343,"about_ca_system_score_gemma":0.0007283652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002842397,"about_ca_topic_score_gemma":0.002218183,"domain_scores_codex":[0.9996535,0.00003338789,0.00001376673,0.0001232608,0.0001109841,0.00006514297],"domain_scores_gemma":[0.9996395,0.00008209841,0.00005982808,0.0000333772,0.0001371659,0.00004800285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005030867,0.0001633168,0.01059289,0.0007188009,0.00009451664,0.001364342,0.001793703,0.002181184,0.6780741,0.1496792,0.01470105,0.1401339],"study_design_scores_gemma":[0.00005761119,0.0003646459,0.03961417,0.0001935691,0.0001408276,0.005327687,0.005181945,0.008202199,0.5601037,0.06701827,0.3136536,0.0001417587],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6336868,0.01067063,0.03049326,0.008882232,0.0009339639,0.00009947344,0.001701319,0.0004169163,0.3131154],"genre_scores_gemma":[0.9424849,0.001770338,0.00865144,0.0008688357,0.0001164315,0.00005415771,0.001205337,0.00008197316,0.04476669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01097381,"threshold_uncertainty_score":0.03671104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190979825910565,"score_gpt":0.2108773159153275,"score_spread":0.1989675176562218,"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."}}