{"id":"W4391774037","doi":"10.5558/tfc2024-004","title":"Tree Improvement in Canada – past, present and future, 2023 and beyond","year":2024,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Forest ecology and management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Newfoundland and Labrador; Government of Nova Scotia; Government of Prince Edward Island; Government of New Brunswick; Natural Resources Canada; Ministère des Ressources naturelles et des Forêts (Québec); Government of Alberta; Government of Manitoba; Government of Canada; Sault College; Ontario Forest Research Institute; Government of Ontario; Ministry of Energy, Northern Development and Mines; Willow Biosciences (Canada); Government of British Columbia; Agriculture and Agri-Food Canada; Ministry of Forests; University of Alberta","funders":"","keywords":"Tree (set theory); Forestry; Environmental science; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001490736,0.0003661916,0.000303938,0.001797227,0.002568488,0.003169796,0.0009567478,0.0008206094,0.005769736],"category_scores_gemma":[0.001355447,0.0001290156,0.0004553623,0.004312025,0.0009879656,0.001072513,0.0009506535,0.001256021,0.0007860838],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05046217,"about_ca_system_score_gemma":0.1402036,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9831809,"about_ca_topic_score_gemma":0.9931907,"domain_scores_codex":[0.9979618,0.00006417714,0.00004198613,0.00008364564,0.00100175,0.0008467486],"domain_scores_gemma":[0.9967044,0.0001018833,0.0002171221,0.000040471,0.001812986,0.001123171],"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":[0.0005956243,0.0002235997,0.1199977,0.002239447,0.0001376119,0.0006020473,0.002020351,0.002722847,0.004919743,0.0165706,0.162613,0.6873575],"study_design_scores_gemma":[0.00002056881,0.0001484094,0.3235734,0.0008041168,0.00006288823,0.0002208624,0.003360925,0.000771707,0.000902751,0.001050258,0.6690212,0.0000628423],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2852842,0.3244609,0.002910159,0.125658,0.002421185,0.0002025051,0.02997937,0.001167004,0.2279166],"genre_scores_gemma":[0.756705,0.148057,0.008613644,0.01261111,0.000410856,0.0000829411,0.01662785,0.0001280235,0.05676351],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9495378,"threshold_uncertainty_score":0.3661304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003908359330158282,"score_gpt":0.1908775093644417,"score_spread":0.1869691500342834,"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."}}