{"id":"W3127630091","doi":"10.1016/j.tfp.2021.100064","title":"Modelling climate effects on diameter growth of red pine trees in boreal Ontario, Canada","year":2021,"lang":"en","type":"article","venue":"Trees Forests and People","topic":"Forest ecology and management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Forest Research Institute; Ministry of Natural Resources and Forestry","funders":"Canadian Forest Service; Ontario Ministry of Natural Resources and Forestry","keywords":"Representative Concentration Pathways; Boreal; Climate change; Taiga; Environmental science; Diameter at breast height; Annual growth %; Physical geography; Dendroclimatology; Red pine; Growth model; Bark (sound); Forestry; Geography; Pinus <genus>; Climatology; Climate model; Atmospheric sciences; Ecology; Mathematics; Botany; Biology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002874937,0.0004712083,0.0002235549,0.0004118029,0.0006851621,0.0008104147,0.0008402186,0.0003895522,0.0008743331],"category_scores_gemma":[0.0006412153,0.0003104489,0.0004645896,0.0006119486,0.000343815,0.0002416193,0.0002717975,0.0002213151,0.0001000371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01678599,"about_ca_system_score_gemma":0.007893355,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9815994,"about_ca_topic_score_gemma":0.9896448,"domain_scores_codex":[0.9998364,0.0000148136,0.00000627651,0.00005295086,0.00003546467,0.00005393146],"domain_scores_gemma":[0.9996425,0.00009940068,0.00005218363,0.00001319014,0.0001449331,0.00004784567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002125457,0.0001056812,0.3869916,0.0001309943,0.0001243348,0.0003636571,0.0007563133,0.5879992,0.007746802,0.0007837905,0.001727269,0.01305783],"study_design_scores_gemma":[0.00004658082,0.00007534045,0.4597205,0.00003613154,0.00009279831,0.00006554243,0.0007353226,0.532963,0.001065421,0.0002222276,0.004920645,0.00005642368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944576,0.0002026172,0.001094351,0.00006217152,0.00000690632,0.00002942087,0.001693337,0.00004661524,0.002407005],"genre_scores_gemma":[0.996909,0.000146878,0.0007568031,0.00001347685,0.000001672767,0.00001645898,0.0008613673,0.0000111731,0.001283205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01840061,"threshold_uncertainty_score":0.1217915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005629636347385609,"score_gpt":0.1853557908553937,"score_spread":0.1797261545080081,"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."}}