{"id":"W3217179662","doi":"10.1007/s13258-021-01187-9","title":"Prospects and challenges of epigenomics in crop improvement","year":2021,"lang":"en","type":"review","venue":"Genes & Genomics","topic":"Plant Molecular Biology Research","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Genetics","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Biology; Epigenomics; Crop; Human genetics; Biotechnology; Agriculture; Computational biology; Genetics; Agronomy; Ecology; DNA methylation; Gene","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":[0.0003563795,0.0002582862,0.001039871,0.00003414611,0.00004422685,0.00002356948,0.0003498376,0.0003343495,0.00001857897],"category_scores_gemma":[0.00002475644,0.0001088028,0.0001697123,0.0001690801,0.00008278601,0.00001465615,0.0004245802,0.0002009821,0.000007600999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008093005,"about_ca_system_score_gemma":0.0001175194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005499264,"about_ca_topic_score_gemma":0.0008559393,"domain_scores_codex":[0.9984462,0.0001461151,0.0004363376,0.0005202142,0.0001016188,0.0003495317],"domain_scores_gemma":[0.9994437,0.0001299547,0.0001947031,0.0001152101,0.00004196468,0.00007451071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003145704,0.00003728663,0.00003748773,0.001228602,0.00004132938,0.00002497671,0.00001930367,3.196749e-7,0.006287361,0.0001797828,0.00000357788,0.9921368],"study_design_scores_gemma":[0.00005248399,0.0001558865,0.0002801036,0.0005837791,0.00004459934,0.00003815024,0.00005436736,0.000001142159,0.0002787322,0.00008890144,0.9981855,0.0002363319],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03098276,0.9680127,1.126028e-7,0.00008990079,0.00006488673,0.0005893237,0.0001722268,0.0000074208,0.0000806592],"genre_scores_gemma":[0.00007633993,0.9993844,0.0001084296,0.00001248347,0.0001100859,0.00005124524,0.0002106725,0.00000307213,0.00004328587],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9981819,"threshold_uncertainty_score":0.4436849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08056222445512613,"score_gpt":0.2903687108670929,"score_spread":0.2098064864119668,"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."}}