Canada and Aichi Biodiversity Target 11: understanding ‘other effective area-based conservation measures’ in the context of the broader target
Bibliographic record
Abstract
A renewed global agenda to address biodiversity loss was sanctioned by adoption of the Strategic Plan for Biodiversity 2011–2020 and the 20 Aichi Biodiversity Targets in 2010 by Parties to the Convention on Biological Diversity. However, Aichi Biodiversity Target 11 contained a significant policy and reporting challenge, conceding that both protected areas (PAs) and ‘other effective area-based conservation measures’ (OEABCMs) could be used to meet national targets of protecting 17 and 10 % of terrestrial and marine areas, respectively. We report on a consensus-based approach used to (1) operationalize OEABCMs in the Canadian context and (2) develop a decision-screening tool to assess sites for inclusion in Canada’s Aichi Target 11 commitment. Participants in workshops determined that for OEABCMs to be effective, they must share a core set of traits with PAs, consistent with the intent of Target 11. (1) Criteria for inclusion of OEABCMs in the Target 11 commitment should be consistent with the overall intent of PAs, with the exception that they may be governed by regimes not previously recognized by reporting agencies. (2) These areas should have an expressed objective to conserve nature, be long-term, generate effective nature conservation outcomes, and have governance regimes that ensure effective management. A decision-screening tool was developed that can reduce the risk that areas with limited conservation value are included in national accounting. The findings are relevant to jurisdictions where the debate on what can count is distracting Parties to the Convention from reaching conservation goals.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".