{"id":"W2169486949","doi":"10.1111/j.1365-2486.2006.01271.x","title":"Use of response functions in selecting lodgepole pine populations for future climates","year":2006,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":288,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of British Columbia; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; BIOCAP Canada","keywords":"Pinus contorta; Reforestation; Productivity; Climate change; Global warming; Range (aeronautics); Environmental science; Ecosystem; Population; Species distribution; Ecology; Physical geography; Agroforestry; Geography; Habitat; Biology; Engineering","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":[0.006888056,0.0009531662,0.0005571919,0.001999699,0.0004076372,0.00101685,0.0007241474,0.0005288981,0.001455061],"category_scores_gemma":[0.02003303,0.0002095256,0.0008844593,0.0009110082,0.0003320347,0.0007340522,0.0006946083,0.0007020543,0.0003438652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007725809,"about_ca_system_score_gemma":0.0005278463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007218199,"about_ca_topic_score_gemma":0.006895331,"domain_scores_codex":[0.9979511,0.001597247,0.00005320608,0.0002250928,0.000101392,0.00007201762],"domain_scores_gemma":[0.9884178,0.009226117,0.0006068557,0.000558067,0.0009318755,0.0002592942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009227647,0.0004139348,0.3893315,0.0001598488,0.0004259253,0.0002396384,0.0005007556,0.4842086,0.007836047,0.00346037,0.001204805,0.1112959],"study_design_scores_gemma":[0.00002754611,0.0002503465,0.07780142,0.00002533403,0.00006382645,0.00006111401,0.0002978659,0.9154427,0.002589057,0.002540954,0.000854054,0.00004578948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7750199,0.00009862347,0.2209626,0.0001074075,0.00001958537,0.0001697149,0.0006530595,0.0009364155,0.002032711],"genre_scores_gemma":[0.9635132,0.00002481047,0.03520969,0.00002491698,0.000004602308,0.0001751184,0.0004952456,0.00006943433,0.0004830174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007218199,"threshold_uncertainty_score":0.03642797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1054723670624279,"score_gpt":0.3168832613644793,"score_spread":0.2114108943020515,"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."}}