{"id":"W2970830508","doi":"10.1101/748178","title":"Genomic history and ecology of the geographic spread of rice","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Institute of Food and Agriculture; Sight Research UK; Fundação para a Ciência e a Tecnologia; Natural Environment Research Council; Life Sciences Research Foundation; Gordon and Betty Moore Foundation; National Science Foundation; Zegar Family Foundation; U.S. Department of Agriculture","keywords":"Biological dispersal; Domestication; Temperate climate; Ecology; Oryza sativa; Biology; Genetic diversity; Abiotic component; Seed dispersal; Demographic history; Geography; Japonica; Genetic variation; Botany; Genetics; Gene; 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":[0.0003128558,0.0001430244,0.0001589328,0.0009470987,0.0002939204,0.0005082989,0.0001963816,0.0002466306,0.001506071],"category_scores_gemma":[0.0006228971,0.0001509544,0.0001722303,0.001043313,0.0004377897,0.0003000182,0.0005386991,0.0004840977,0.0002871373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003481051,"about_ca_system_score_gemma":0.0001628648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002674006,"about_ca_topic_score_gemma":0.003363598,"domain_scores_codex":[0.9998915,0.00001770057,0.000004343648,0.0000564923,0.00001461795,0.00001531245],"domain_scores_gemma":[0.9996535,0.00008785957,0.0001110399,0.00003627178,0.00005866864,0.00005277135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004679376,0.00004801763,0.7427718,0.0001794076,0.0002606371,0.0006947836,0.002808157,0.006580797,0.1839084,0.00640239,0.001419067,0.05445871],"study_design_scores_gemma":[0.00000983811,0.00003233732,0.9888799,0.00002440128,0.00004575837,0.000247405,0.0004087718,0.0025439,0.003087067,0.001296192,0.003405094,0.00001927458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960548,0.0002677813,0.00149957,0.0001049676,0.000004682247,0.000002207114,0.0005353047,0.00002309265,0.00150771],"genre_scores_gemma":[0.9978956,0.0001898026,0.0006585692,0.00003201198,0.00001035303,0.000002711596,0.0008138735,0.00001435454,0.000382728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002674006,"threshold_uncertainty_score":0.005316854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009223261661632585,"score_gpt":0.1810191568781779,"score_spread":0.1717958952165453,"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."}}