{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005775822,0.0005782045,0.0008856383,0.001761818,0.0006903204,0.002788262,0.0008256036,0.0017431,0.003728359],"category_scores_gemma":[0.004212314,0.0003508473,0.0006009507,0.003301793,0.002342188,0.004200022,0.00179603,0.003657448,0.0008177976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001154496,"about_ca_system_score_gemma":0.001078044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001221768,"about_ca_topic_score_gemma":0.001730451,"domain_scores_codex":[0.9988987,0.0005060364,0.00006106412,0.0002319286,0.0001894127,0.0001126832],"domain_scores_gemma":[0.9956566,0.003129508,0.0003008151,0.0002847834,0.0002478168,0.0003804705],"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":[0.0002060036,0.000133515,0.006533353,0.002012544,0.0001100301,0.0004339245,0.002504122,0.005917624,0.07472556,0.155557,0.01997459,0.7318918],"study_design_scores_gemma":[0.00003654338,0.0002399413,0.02618109,0.001291169,0.00008512673,0.001167245,0.00147939,0.006621034,0.01815437,0.2198338,0.724731,0.0001792059],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.07162323,0.3354754,0.4149508,0.1093597,0.004969499,0.0002004447,0.002432379,0.001471346,0.05951721],"genre_scores_gemma":[0.2604579,0.3202121,0.387699,0.01168055,0.006507339,0.0002394688,0.002447484,0.0004650621,0.01029109],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005775822,"threshold_uncertainty_score":0.03054583,"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."}}