{"id":"W205785495","doi":"","title":"Improving Drought Tolerance of Rice by Designed QTL Pyramiding","year":2007,"lang":"en","type":"article","venue":"分子植物育种","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drought tolerance; Agronomy; Environmental science; Biology; Biotechnology","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":[0.0002781092,0.000531185,0.0004723893,0.0004113312,0.0001891281,0.0003004476,0.0005136888,0.0002570442,0.00140775],"category_scores_gemma":[0.0002631347,0.0003276096,0.00041303,0.000343864,0.000258305,0.0002037484,0.0003756983,0.0007738143,0.0003491198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003965362,"about_ca_system_score_gemma":0.0003359107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000905033,"about_ca_topic_score_gemma":0.001417219,"domain_scores_codex":[0.9998455,0.00001784859,0.00001564458,0.00005496132,0.00003483248,0.00003126877],"domain_scores_gemma":[0.999867,0.00003071589,0.00003376593,0.00001682345,0.00001818567,0.0000334644],"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":[0.00008428511,0.00004585409,0.0002922701,0.00002991555,0.00001213134,0.000049963,0.00002260355,0.0003313519,0.9945365,0.0002063152,0.00005103143,0.0043378],"study_design_scores_gemma":[0.0001027588,0.0002248789,0.005088265,0.000004461839,0.0001248251,0.0001892744,0.0000275977,0.005358889,0.9854966,0.0002124217,0.003145006,0.00002495463],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9074969,0.0003951627,0.08841315,0.0002340792,0.00009557819,0.0001317361,0.0008173228,0.001231951,0.001184209],"genre_scores_gemma":[0.9607849,0.0003026043,0.03376163,0.0001242366,0.00002407466,0.00007669209,0.0009483265,0.0001755476,0.003802128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00140775,"threshold_uncertainty_score":0.004709363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687977849526785,"score_gpt":0.2234947859675004,"score_spread":0.2066150074722325,"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."}}