Protected area targets: Spatially evaluating progress and prioritizing areas to reach 30 × 30 in Canada
Bibliographic record
Abstract
Abstract Protected and conserved areas (PCAs) continue to be a cornerstone of nature conservation, with several international agreements and frameworks setting targets to increase their global coverage. However, the focus on area‐based expansion has resulted in drawbacks related to the quality of PCAs, including widespread gaps in species protection and connectivity. Here, we temporally evaluate progress in terrestrial and freshwater PCA coverage in Canada and associated biophysical component indicators (i.e., ProtConn, Species Protection Index, Key Biodiversity Area [KBA] coverage) under Target 3 of the Kunming‐Montreal Global Biodiversity Framework (GBF). Our analysis reveals progress made from 2010 to 2022, while outlining gaps where accelerated action is needed to deliver upon both the quantity and quality of PCAs. Large gaps in PCA coverage and associated Target 3 component indicators were prevalent in the Northern Arctic, Prairie and Mixedwood Plains ecozones. Further, we systematically prioritize areas for protection that could maximize targets for headline and component indicators under Target 3 of the GBF. Our findings build upon a history of spatial conservation efforts in Canada and offers a novel lens—contextualized within the commitments of the GBF—to advance conservation planning and implementation for achieving 30% protection by 2030 nationally.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".