A climate-resilient marine conservation network for Canada
Bibliographic record
Abstract
Climate change and biodiversity loss are twin crises that are driving global marine conservation efforts. However, if unaccounted for, climate change can undermine the efficacy of such efforts. Despite this, integration of climate change adaptation and resilience into spatial marine conservation and management has been limited in Canada and elsewhere. With climate change impacts becoming increasingly severe, now is the time to anticipate and reduce impacts wherever possible. We provide five recommendations for an inclusive, proactive, climate-ready approach for Canada’s growing marine conservation network: (1) integrating climate-resilience as a universal objective of the Canadian Marine Conservation Network, creating and implementing (2) national transdisciplinary working groups with representation from all knowledge holders and (3) necessary tools that integrate climate change into conservation design, (4) defining operational and climate-relevant monitoring and management objectives, and (5) strengthening communication and increasing knowledge exchange around the roles and benefits of protected areas within government and towards the public. Canada’s extensive marine and coastal areas reflect national and international responsibility to engage on this issue. Canada is well positioned to assume a leading role in climate change adaptation for marine conservation and help accelerate progress towards international commitments around mitigating ongoing biodiversity loss and climate change.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".