{"id":"W3109534956","doi":"10.3390/genes11111387","title":"GWAS Based on RNA-Seq SNPs and High-Throughput Phenotyping Combined with Climatic Data Highlights the Reservoir of Valuable Genetic Diversity in Regional Tomato Landraces","year":2020,"lang":"en","type":"article","venue":"Genes","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"European Social Fund; Università degli Studi di Sassari; National Science Foundation","keywords":"Biology; Genome-wide association study; Quantitative trait locus; Context (archaeology); Single-nucleotide polymorphism; Genetic diversity; Genetics; Candidate gene; Genomics; Genetic variation; Genetic architecture; Association mapping; Genome; Phenotypic trait; Computational biology; Evolutionary biology; Phenotype; Gene; Genotype; Population","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":[0.00009521495,0.0001169181,0.000154167,0.00002456376,0.000165436,0.00001773348,0.0003751282,0.00007372093,0.00002035633],"category_scores_gemma":[0.00002316788,0.00008306433,0.0000204737,0.0001105931,0.00009566292,0.00000711259,0.0004797177,0.00005513953,0.000002149438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004311081,"about_ca_system_score_gemma":0.00003679195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001712992,"about_ca_topic_score_gemma":0.0002415988,"domain_scores_codex":[0.999131,0.00009102639,0.0001350052,0.0003141063,0.0001980832,0.0001308053],"domain_scores_gemma":[0.9993668,0.00002903166,0.00009115618,0.0004147222,0.00004571592,0.00005261042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005694796,0.0002980329,0.7446156,0.0008582466,0.0004576651,0.00004863398,0.003623413,0.1769017,0.04841948,0.001357579,0.01384311,0.00388179],"study_design_scores_gemma":[0.008913162,0.002260149,0.8612334,0.0002089316,0.0003365981,0.00001813058,0.0014095,0.05216335,0.04860391,0.001300321,0.02254906,0.001003498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960351,0.0005298453,0.0003575908,0.002713693,0.0000402332,0.0001765308,0.0000625951,0.000006766872,0.00007765248],"genre_scores_gemma":[0.9965459,0.0001428156,0.002332803,0.0006730462,0.00005700008,0.000001798905,0.0002107025,0.000007067764,0.0000288289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1247383,"threshold_uncertainty_score":0.3387265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05274733689071046,"score_gpt":0.247397063291894,"score_spread":0.1946497264011836,"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."}}