{"id":"W2561122916","doi":"10.1111/mec.13963","title":"Advances in ecological genomics in forest trees and applications to genetic resources conservation and breeding","year":2016,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Forest ecology and management","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Office National des Forêts; Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche; Recherches Avancées sur la Biologie de l’Arbre et les Ecosystèmes Forestiers; Horizon 2020; Federal Circuit Bar Association; City of Hamilton","keywords":"Biology; Genomics; Ecology; Conservation biology; Genetic resources; Conservation genetics; Environmental resource management; Agroforestry; Genome; Microsatellite; Biotechnology; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001311067,0.00006188875,0.00009109289,0.00006228191,0.00003888427,0.000005548926,0.00007637542,0.00006510825,0.0000704941],"category_scores_gemma":[0.00003929227,0.00005060075,0.000006593145,0.00009215993,0.0001766805,0.00005466044,0.0001908998,0.00003430283,0.00002975045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008359311,"about_ca_system_score_gemma":0.000002886458,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004515671,"about_ca_topic_score_gemma":0.03574844,"domain_scores_codex":[0.9993875,0.00004118875,0.0001275486,0.000241997,0.00003068949,0.0001710642],"domain_scores_gemma":[0.9997774,0.00007905845,0.00002806668,0.00007339431,0.000001835388,0.00004021985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001172554,0.00003401382,0.9818721,0.00000266514,0.000001313223,0.00001737287,0.00006324072,0.001251429,0.002614059,0.003974296,0.00002767806,0.01013011],"study_design_scores_gemma":[0.0003063075,0.0001383211,0.9703307,0.000002417617,0.000002739167,0.000004716491,0.0000301178,0.0002051064,0.00004070312,0.01498839,0.01388427,0.00006620775],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934129,0.00008651512,0.003213624,0.001898058,0.00001596725,0.0003781809,7.735451e-7,0.000007734237,0.0009862753],"genre_scores_gemma":[0.9974082,0.0001994798,0.001254851,0.0008666955,0.000005035272,0.0002200282,6.264129e-7,0.000003937788,0.00004111362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03570328,"threshold_uncertainty_score":0.9818466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005051951484533622,"score_gpt":0.2070941267314289,"score_spread":0.2020421752468953,"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."}}