{"id":"W2699315399","doi":"10.1111/pbi.12770","title":"Uncovering the dispersion history, adaptive evolution and selection of wheat in China","year":2017,"lang":"en","type":"article","venue":"Plant Biotechnology Journal","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; Agricultural Research Service; National Supercomputer Centre, Linköpings Universitet; University of California, Davis; Sichuan Agricultural University; University of Haifa; Sun Yat-sen University; National Natural Science Foundation of China; National Supercomputer Centre in Guangzhou; U.S. Department of Agriculture","keywords":"Biology; Deserts and xeric shrublands; Selection (genetic algorithm); China; Demographic history; Population; Genome; Adaptive evolution; Evolutionary biology; Ecology; Gene; Genetics; Genetic variation; Geography; Demography","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":[0.0001991464,0.0000540907,0.00009028431,0.00002145091,0.0002933328,0.00001370425,0.0001611033,0.0001481246,0.0000244549],"category_scores_gemma":[0.00002344834,0.00001822564,0.00002349261,0.00003028126,0.000154311,0.00004704971,0.00005419632,0.0002374287,6.886147e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004312755,"about_ca_system_score_gemma":0.00000700308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003598986,"about_ca_topic_score_gemma":0.001471263,"domain_scores_codex":[0.9996086,0.0000301167,0.0001036818,0.00009008356,0.00005060447,0.0001169629],"domain_scores_gemma":[0.9998053,0.0000195087,0.0001166473,0.00002851313,0.00001225355,0.00001783377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006774336,0.000024901,0.05661609,0.000001622599,0.000006258849,0.000008693474,0.00005405015,0.000006876857,0.9003646,0.0009216767,0.0001621343,0.04176538],"study_design_scores_gemma":[0.0001676189,0.0005007256,0.9774752,0.0000336068,0.000008861904,0.000609646,0.0001314015,0.0004232563,0.0142963,0.002512131,0.003763536,0.00007767457],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995953,0.001079778,0.00002313339,0.002575262,0.0001499147,0.00004147908,0.000006576681,0.000006203976,0.000164698],"genre_scores_gemma":[0.9988335,0.001016811,0.00004430044,0.000009980655,0.00006786903,7.512517e-7,0.000001291682,2.977291e-7,0.00002518548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9208592,"threshold_uncertainty_score":0.2256108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0146413015005704,"score_gpt":0.1930076025011793,"score_spread":0.1783663010006089,"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."}}