{"id":"W4319444318","doi":"10.3389/ffgc.2023.1084797","title":"Predicting the global fundamental climate niche of lodgepole pine for climate change adaptation","year":2023,"lang":"en","type":"article","venue":"Frontiers in Forests and Global Change","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Climate change; Pinus contorta; Niche; Adaptation (eye); Ecological niche; Environmental science; Population; Climate model; Species distribution; Ecology; Biology","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":[0.0009594374,0.0004891104,0.0003751901,0.000342136,0.00028855,0.0005822789,0.0005723087,0.0004052739,0.000680001],"category_scores_gemma":[0.001436795,0.000163925,0.0006186542,0.0002300353,0.0002582498,0.0005543491,0.0003476382,0.0003109933,0.00009145073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005611417,"about_ca_system_score_gemma":0.0007553966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02282298,"about_ca_topic_score_gemma":0.03075807,"domain_scores_codex":[0.9998333,0.00006497432,0.000004644978,0.00006197877,0.00001123658,0.00002379132],"domain_scores_gemma":[0.999603,0.0002701704,0.00003951423,0.00002476735,0.00003292966,0.0000296643],"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":[0.0001316781,0.0001007205,0.08043812,0.00009495892,0.0001475123,0.0001231543,0.000201837,0.8757293,0.007516785,0.002119828,0.0003073912,0.03308876],"study_design_scores_gemma":[0.000009044498,0.00007302171,0.02371228,0.00001257785,0.00005233858,0.00003790991,0.0000875928,0.9726259,0.000642259,0.002042631,0.0006855992,0.00001878782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9197841,0.0004177243,0.07820167,0.0001233733,0.00001445205,0.00002139221,0.0003114048,0.0002011212,0.0009249187],"genre_scores_gemma":[0.9922993,0.00007144659,0.007202763,0.00001745649,0.000005170581,0.00001484426,0.0001797524,0.00001530872,0.0001939976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02282298,"threshold_uncertainty_score":0.04538023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05055450105275047,"score_gpt":0.2818076784201382,"score_spread":0.2312531773673877,"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."}}