{"id":"W2565766239","doi":"10.1002/rse2.34","title":"A conservation assessment of Canada's boreal forest incorporating alternate climate change scenarios","year":2016,"lang":"en","type":"article","venue":"Remote Sensing in Ecology and Conservation","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Forest Service; Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Nature Conservancy; Nature Conservancy of Canada; University of British Columbia; Ivey Foundation","keywords":"Climate change; Boreal; Vegetation (pathology); Environmental science; Wilderness area; Baseline (sea); Conservation Reserve Program; Taiga; Environmental resource management; Wilderness; Ecology; Geography; Forestry","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":[0.001028838,0.0003822409,0.0001910424,0.0007223688,0.001117551,0.0009123338,0.0007710419,0.0003193086,0.0007031679],"category_scores_gemma":[0.001512531,0.0001518275,0.0003735334,0.0007301074,0.0005663297,0.0003935506,0.0004036472,0.0002874613,0.00003172374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01375334,"about_ca_system_score_gemma":0.006266795,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8579153,"about_ca_topic_score_gemma":0.9234545,"domain_scores_codex":[0.9996105,0.0001156425,0.00001229084,0.00004172905,0.000124162,0.000095697],"domain_scores_gemma":[0.998936,0.0002102513,0.0001303166,0.00005683384,0.0004349692,0.0002316885],"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":[0.0003435797,0.0001230167,0.2024249,0.00004208456,0.0001340377,0.0003568934,0.0001176704,0.778569,0.001591071,0.003270975,0.00116178,0.01186488],"study_design_scores_gemma":[0.00007525879,0.000320292,0.2016946,0.00001691708,0.00008793863,0.00009380733,0.000792908,0.7923318,0.0008338406,0.001669271,0.002036112,0.00004722942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948772,0.00004428571,0.001100175,0.0001522409,0.000003802549,0.00004691915,0.0005100495,0.00002607878,0.003239227],"genre_scores_gemma":[0.9984863,0.00002213267,0.001014389,0.00001294317,0.000001106052,0.000009171693,0.0001748678,0.000001531662,0.0002775353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1420847,"threshold_uncertainty_score":0.2858428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456752846792211,"score_gpt":0.2417491844076194,"score_spread":0.2271816559396973,"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."}}