{"id":"W3084260795","doi":"10.3390/f11090976","title":"Advances and Promises of Epigenetics for Forest Trees","year":2020,"lang":"en","type":"article","venue":"Forests","topic":"Plant Molecular Biology Research","field":"Agricultural and Biological Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université du Québec en Outaouais","funders":"Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro; Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior; Agence Nationale de la Recherche","keywords":"Epigenetics; Tree (set theory); Adaptation (eye); Field (mathematics); Forest management; Biology; Ecology; Environmental resource management; Neuroscience","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":[0.00004668756,0.00005279699,0.00009814087,0.000003329052,0.00004126782,0.000007590738,0.0001132819,0.00004126385,0.00001085521],"category_scores_gemma":[0.0001418496,0.00001896197,0.00002982534,0.0000675725,0.00007853803,0.00002624358,0.00004441828,0.00002707023,0.00000175261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001254646,"about_ca_system_score_gemma":0.000004177275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008521692,"about_ca_topic_score_gemma":0.002504627,"domain_scores_codex":[0.9995744,0.00001678334,0.00008305018,0.0001337093,0.00006152919,0.0001305453],"domain_scores_gemma":[0.9997122,0.0001410251,0.0000324748,0.00001713199,0.00003105629,0.00006609003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001364456,0.00002312204,0.6468403,0.00004715101,0.000009138133,0.000002325941,0.00004059897,0.00001185431,0.2905333,0.00034118,0.0003361848,0.06167836],"study_design_scores_gemma":[0.0002024912,0.001291752,0.9439956,0.00001218584,0.000007172722,0.000003262738,0.00003923582,0.000467885,0.02065916,0.002073916,0.03114719,0.000100157],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972771,0.001016167,0.00003315067,0.001204496,0.00001126317,0.0002380903,0.00009433606,0.00001228282,0.0001131201],"genre_scores_gemma":[0.9993218,0.0002061715,0.0002567564,0.00006360595,0.00006130576,0.0000186802,0.00004992554,4.497102e-7,0.00002135536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2971553,"threshold_uncertainty_score":0.1397641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03113229019833664,"score_gpt":0.2585407715690278,"score_spread":0.2274084813706911,"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."}}