{"id":"W4285093403","doi":"10.1038/s41422-022-00685-z","title":"A super pan-genomic landscape of rice","year":2022,"lang":"en","type":"article","venue":"Cell Research","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":368,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agricultural Science and Technology Innovation Program; Basic and Applied Basic Research Foundation of Guangdong Province; University of Chinese Academy of Sciences; China Postdoctoral Science Foundation; Chinese Academy of Sciences; Agricultural Research Service; Chinese Academy of Agricultural Sciences; China Agricultural University; Institute of Genetics; National Natural Science Foundation of China; U.S. Department of Agriculture","keywords":"Biology; Domestication; Genome; Gene; Genetics; Evolutionary biology; Genetic diversity; Adaptation (eye); Nucleotide diversity; Genomics; Haplotype; Computational biology; Allele; Population","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000213654,0.0002278284,0.0002745413,0.000672505,0.0002410018,0.0003261617,0.000250578,0.0002122807,0.001884126],"category_scores_gemma":[0.0005645741,0.0002285071,0.0003375903,0.001130481,0.0002064475,0.0004911637,0.0004624664,0.000399448,0.0003272887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576573,"about_ca_system_score_gemma":0.0002263355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009632923,"about_ca_topic_score_gemma":0.002870394,"domain_scores_codex":[0.9998716,0.00001741066,0.000005479823,0.0000731652,0.00001989518,0.00001240231],"domain_scores_gemma":[0.9997204,0.0001103363,0.00004531138,0.00005759796,0.00003858075,0.00002772382],"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":[0.000636183,0.00004905992,0.02157353,0.0005980143,0.0001839758,0.0006982799,0.0005810349,0.01928694,0.8977683,0.007961697,0.001648742,0.04901429],"study_design_scores_gemma":[0.0001571771,0.0009270303,0.450258,0.0001749997,0.0007398489,0.004426247,0.00102828,0.1808998,0.1770967,0.04616264,0.1379697,0.0001595458],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8518481,0.000727551,0.1230866,0.0001715805,0.00002460762,0.0001048635,0.01547425,0.001848648,0.006713754],"genre_scores_gemma":[0.8601708,0.0005279375,0.1080641,0.0002073208,0.00002025181,0.0001612243,0.02846334,0.0006101347,0.00177477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001884126,"threshold_uncertainty_score":0.006303072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610228592549965,"score_gpt":0.3041171060157801,"score_spread":0.2680148200902804,"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."}}