{"id":"W1525208350","doi":"","title":"水稻幼苗对多浓度Fe^2＋胁迫的QTL联合检测","year":2007,"lang":"zh","type":"article","venue":"分子植物育种","topic":"Plant Micronutrient Interactions and Effects","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantitative trait locus; Biology; Genetics; Gene","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":[0.0005567762,0.0006135844,0.0005361767,0.0007328874,0.0006678202,0.001239564,0.0006225078,0.0006591672,0.007421235],"category_scores_gemma":[0.0006422015,0.0007516078,0.0008184115,0.000606412,0.0009184329,0.0009990747,0.0006816396,0.001414395,0.00137143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002314566,"about_ca_system_score_gemma":0.001247669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01691272,"about_ca_topic_score_gemma":0.01490469,"domain_scores_codex":[0.9993464,0.00007328322,0.0000397713,0.0002752537,0.0001718277,0.00009347002],"domain_scores_gemma":[0.9995921,0.0001275856,0.00006785252,0.00003784901,0.0001179616,0.00005670723],"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":[0.00058709,0.0001233612,0.01374731,0.0002454474,0.0001508947,0.0003577879,0.0006535231,0.002365938,0.9317654,0.01099424,0.001088409,0.03792075],"study_design_scores_gemma":[0.0003914417,0.0007094573,0.2440321,0.0001685451,0.0007345793,0.0009783775,0.001521743,0.01166802,0.662905,0.01448518,0.06209194,0.0003136295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7973676,0.00197645,0.1234631,0.001213641,0.0002088367,0.0003356764,0.004655927,0.001442483,0.06933631],"genre_scores_gemma":[0.9062808,0.0005407117,0.04926467,0.0004079583,0.00002511778,0.0002640829,0.002239066,0.0004024583,0.04057502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01691272,"threshold_uncertainty_score":0.03362858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653135007766524,"score_gpt":0.2357032046099025,"score_spread":0.2191718545322372,"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."}}