{"id":"W2060773447","doi":"10.1016/j.foreco.2010.11.012","title":"Using a spatiotemporal climate model to assess population-level Douglas-fir growth sensitivity to climate change across large climatic gradients in British Columbia, Canada","year":2010,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of British Columbia; University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; U.S. Department of Commerce","keywords":"Climate change; Precipitation; Douglas fir; Climate sensitivity; Environmental science; Range (aeronautics); Population; Productivity; Physical geography; Dendrochronology; Ecology; Geography; Provenance; Climatology; Climate model; Biology; Forestry; Meteorology; 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":[0.0007398221,0.0003739205,0.0003691797,0.0005697262,0.001615061,0.001359704,0.001140814,0.0005370025,0.001279839],"category_scores_gemma":[0.002691443,0.0004279669,0.0004283209,0.001150593,0.0005003981,0.0004504719,0.0004513549,0.0005437165,0.0001398703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02124636,"about_ca_system_score_gemma":0.01291195,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9952272,"about_ca_topic_score_gemma":0.9961125,"domain_scores_codex":[0.9997585,0.00005151687,0.00001713091,0.00007311138,0.00002603378,0.00007366246],"domain_scores_gemma":[0.9991008,0.0002911737,0.00008488213,0.00005299932,0.000328848,0.0001413304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003312925,0.0002160006,0.5920243,0.00004178835,0.0003462769,0.0001822202,0.0003375532,0.3934457,0.001257406,0.0008434225,0.002009022,0.008965005],"study_design_scores_gemma":[0.00009255116,0.00003330698,0.3148547,0.00002169076,0.0001548258,0.00003837268,0.0007831536,0.6821375,0.0003652014,0.0003728916,0.001091395,0.00005445563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981905,0.00005727365,0.0003138907,0.0001030733,0.000005150516,0.000009325338,0.0007141692,0.0000227509,0.0005838918],"genre_scores_gemma":[0.9983696,0.0000586009,0.00044195,0.00002171478,0.000001454087,0.00000760177,0.0005535405,0.000007460983,0.0005382015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02124636,"threshold_uncertainty_score":0.1541539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04450798875478095,"score_gpt":0.272055648672773,"score_spread":0.227547659917992,"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."}}