{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003821565,0.0002022869,0.0002894643,0.001303246,0.0004993094,0.0003303193,0.0002860334,0.0001721346,0.0003525719],"category_scores_gemma":[0.0003464169,0.0001770606,0.0003264622,0.001714589,0.0003998269,0.0002194983,0.0005187124,0.0002026688,0.00006327099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005782058,"about_ca_system_score_gemma":0.0004541687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327533,"about_ca_topic_score_gemma":0.01932826,"domain_scores_codex":[0.9997966,0.00002353882,0.00001500267,0.00008193155,0.00003925174,0.00004373464],"domain_scores_gemma":[0.9997813,0.0000435202,0.00006543438,0.00003452213,0.00002716885,0.00004802092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001606853,0.00003964036,0.8603763,0.00006774379,0.000183048,0.0003996081,0.002394289,0.0008745213,0.1085297,0.0004438247,0.0001074581,0.02642318],"study_design_scores_gemma":[0.000002669244,0.00001502746,0.9988642,0.000001599628,0.00001274632,0.00004373679,0.0001171402,0.0003517403,0.0003723551,0.00006348427,0.0001509562,0.00000426376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996973,0.00005754712,0.00007461994,0.000008452438,3.113717e-7,0.00000130212,0.00003854624,0.000001776793,0.0001200102],"genre_scores_gemma":[0.999559,0.00006476301,0.0001201445,0.000008714711,0.000001342632,0.000002875402,0.0001404423,0.000001933016,0.0001008164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01327533,"threshold_uncertainty_score":0.0263961,"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."}}