{"id":"W4400965562","doi":"10.1007/s00122-024-04696-9","title":"Mining for QTL controlling maize low-phosphorus response genes combined with deep resequencing of RIL parental genomes and in silico GWAS analysis","year":2024,"lang":"en","type":"article","venue":"Theoretical and Applied Genetics","topic":"Plant nutrient uptake and metabolism","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Postdoctoral Research Foundation of China; National Natural Science Foundation of China","keywords":"Biology; Quantitative trait locus; Genetics; Candidate gene; Population; Gene; Genome-wide association study; In silico; Transcriptome; Single-nucleotide polymorphism; Genotype; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004465217,0.0001476834,0.0003594025,0.00004423495,0.00008003432,0.00005628228,0.00008717282,0.0000809376,0.00001831922],"category_scores_gemma":[0.00001964223,0.0000613319,0.00005651528,0.0003808116,0.0003205295,0.00001743132,0.00004692729,0.00006886246,4.700385e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007238085,"about_ca_system_score_gemma":0.000009250701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005503419,"about_ca_topic_score_gemma":0.00003905227,"domain_scores_codex":[0.9989989,0.00004949862,0.0002500495,0.0003066341,0.0001333522,0.0002615818],"domain_scores_gemma":[0.9989555,0.0008343961,0.00004093679,0.00004640351,0.00002382777,0.00009892885],"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":[0.01216022,0.0001318206,0.01666394,0.00008406807,0.0003333589,0.0000245854,0.001009744,0.0004786127,0.7145094,0.09885969,0.000008398323,0.1557362],"study_design_scores_gemma":[0.008510575,0.004584915,0.2932114,0.0005597948,0.003723029,0.00006743535,0.01042391,0.1117255,0.4549541,0.09520622,0.01450639,0.00252666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882987,0.01088653,0.0001210631,0.00022091,0.00002175722,0.0002812702,0.00007458531,0.00002081432,0.00007437167],"genre_scores_gemma":[0.9969884,0.002086888,0.0007320827,0.00005551855,0.0000527164,0.00003004413,0.00003469935,0.000002185691,0.00001745365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2765475,"threshold_uncertainty_score":0.2501042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009139415808430697,"score_gpt":0.2131139572865031,"score_spread":0.2039745414780724,"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."}}