{"id":"W2212538009","doi":"10.1016/j.foreco.2015.11.004","title":"Predicting softwood quality attributes from climate data in interior British Columbia, Canada","year":2015,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Softwood; Environmental science; Climate change; Dendrochronology; Forest inventory; Physical geography; Forestry; Agroforestry; Forest management; Geography; Ecology; Pulp and paper industry; Engineering","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":[0.0007278937,0.0004438275,0.0003873757,0.001163616,0.001946435,0.001663273,0.001097607,0.000464796,0.002222781],"category_scores_gemma":[0.002100805,0.0002773119,0.0003368409,0.002247695,0.0005146026,0.0003958642,0.0006097686,0.0007301432,0.000423382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02224903,"about_ca_system_score_gemma":0.02108475,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9982407,"about_ca_topic_score_gemma":0.9994397,"domain_scores_codex":[0.9996351,0.0000371898,0.00002432033,0.00008328813,0.00008667834,0.0001334357],"domain_scores_gemma":[0.9977888,0.0002900272,0.0001441265,0.00007245517,0.001246304,0.0004581848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002093043,0.0001455628,0.9558553,0.00005828244,0.0001479105,0.0001700079,0.0005838227,0.008977154,0.0009977889,0.0002594832,0.007279156,0.02531619],"study_design_scores_gemma":[0.00002436466,0.00001066652,0.9822091,0.0000535681,0.00005215944,0.00002500426,0.001656209,0.01287501,0.0002794493,0.0001012369,0.002688531,0.00002459492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923323,0.0004974686,0.0002521156,0.0002469675,0.00001676694,0.00002405914,0.004175269,0.0000309507,0.002424035],"genre_scores_gemma":[0.993419,0.0004036509,0.0004585731,0.00007348618,0.000004149276,0.00001497151,0.002742579,0.00001409166,0.002869522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02224903,"threshold_uncertainty_score":0.1614287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03390032991224026,"score_gpt":0.2449769109992636,"score_spread":0.2110765810870233,"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."}}