{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042003,0.00008957505,0.0001586202,0.00004217228,0.0001486311,0.00000413953,0.00003888393,0.00008271428,0.000006832354],"category_scores_gemma":[0.0001176062,0.00007579038,0.00001161919,0.0001066336,0.0002008459,0.0001328452,0.00007474558,0.00007074383,0.000001399048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002067555,"about_ca_system_score_gemma":0.00006335318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1154532,"about_ca_topic_score_gemma":0.957477,"domain_scores_codex":[0.9991362,0.0001126402,0.0002837068,0.0001878718,0.00008601446,0.0001935743],"domain_scores_gemma":[0.9993241,0.0002883854,0.000246531,0.00008225099,0.0000315273,0.00002723514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001687209,0.00000899002,0.9834704,0.00001216683,0.000007453111,0.00001237607,0.0000887818,0.0001091469,0.0007187605,0.001206053,0.0000321001,0.01431692],"study_design_scores_gemma":[0.0003557334,0.00003563666,0.7841954,0.00003540429,0.000006337008,0.00001339941,0.00002951162,0.2121432,0.0000272718,0.003069435,0.00002075512,0.00006792365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881441,0.000004984292,0.001176545,0.008824167,0.0001314595,0.000195782,0.000003758595,0.00001088141,0.001508342],"genre_scores_gemma":[0.9944932,0.00005415899,0.004038897,0.001356482,0.00001274697,0.000001143073,0.0000072998,0.000005418359,0.00003066975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8420237,"threshold_uncertainty_score":0.890437,"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."}}