{"id":"W2901601054","doi":"10.3390/land7040137","title":"Context and Opportunities for Expanding Protected Areas in Canada","year":2018,"lang":"en","type":"article","venue":"Land","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Natural Resources Canada; McGill University; Canadian Forest Service","funders":"Canadian Forest Service; Natural Resources Canada; Canadian Space Agency; U.S. Forest Service","keywords":"De facto; Protected area; Context (archaeology); Stakeholder; Environmental resource management; Geography; Environmental planning; Work (physics); Indigenous; Identification (biology); Convention on Biological Diversity; Business; Environmental protection; Political science; Biodiversity; Ecology; Public relations; Engineering","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":[0.00004967627,0.00003144623,0.00003743375,0.00001076613,0.00008068301,0.00001028302,0.00003576505,0.000008629644,0.0001616806],"category_scores_gemma":[0.000006852656,0.00002648079,0.00000399037,0.00002423372,0.00004713932,0.00002541335,0.00005100012,0.0000123825,0.000001988597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007981332,"about_ca_system_score_gemma":0.00001431227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7990057,"about_ca_topic_score_gemma":0.9776223,"domain_scores_codex":[0.9997495,0.000007673805,0.00004062141,0.00007353007,0.00005284236,0.0000758521],"domain_scores_gemma":[0.9999046,0.00001310932,0.00001456842,0.00003906219,0.000002999519,0.00002566117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001087877,0.00000274093,0.9919629,0.000005038149,0.000001986433,0.00000249991,0.000287077,0.000001479783,0.00003058241,0.000001983678,0.0007451386,0.006947656],"study_design_scores_gemma":[0.0002895517,0.00001938365,0.9375594,0.000006876612,0.000002730726,5.363705e-7,0.001422504,0.0003285675,0.00004944498,0.00003770816,0.06023553,0.00004782073],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972897,0.00001107932,0.00004117027,0.0006849751,0.00002939281,0.0001766609,0.000006532023,0.000004393825,0.001756053],"genre_scores_gemma":[0.9984531,0.000005891266,0.00004021339,0.00038279,0.00001330448,0.000007847662,0.000002810943,0.000001342948,0.001092735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1786167,"threshold_uncertainty_score":0.2023328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03721415099751806,"score_gpt":0.207308202323419,"score_spread":0.170094051325901,"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."}}