Emerging issues for protected and conserved areas in Canada
Bibliographic record
Abstract
Horizon scanning is increasingly used in conservation to systematically explore emerging policy and management issues. We present the results of a horizon scan of issues likely to impact management of Canadian protected and conserved areas over the next 5–10 years. Eighty-eight individuals participated, representing a broad community of academics, government and nongovernment organizations, and foundations, including policymakers and managers of protected and conserved areas. This community initially identified 187 issues, which were subsequently triaged to 15 horizon issues by a group of 33 experts using a modified Delphi technique. Results were organized under four broad categories: ( i) emerging effects of climate change in protected and conserved areas design, planning, and management (i.e., large-scale ecosystem changes, species translocation, fire regimes, ecological integrity, and snow patterns); ( ii) Indigenous governance and knowledge systems (i.e., Indigenous governance and Indigenous knowledge and Western science); ( iii) integrated conservation approaches across landscapes and seascapes (i.e., connectivity conservation, integrating ecosystem values and services, freshwater planning); and ( iv) early responses to emerging cumulative, underestimated, and novel threats (i.e., management of cumulative impacts, declining insect biomass, increasing anthropogenic noise, synthetic biology). Overall, the scan identified several emerging issues that require immediate attention to effectively reduce threats, respond to opportunities, and enhance preparedness and capacity to react.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".