Recovery Planning for Pacific Marine Species at Risk in the Wake of Climate Change and Ocean Acidification: Canadian Practice, Future Courses
Bibliographic record
Abstract
This article evaluates how Canadian recovery planning for Pacific marine species at risk incorporates two pressing 21st century concerns: global climate change and ocean acidification (OA). While many recovery strategies for Pacific species at risk show some understanding of climate change or OA, they generally fail to incorporate key climate and OA information or to consider how these two issues will actually affect the species in question. Two strategies for progress are suggested. First is an administrative strategy that includes the development of a national climate change adaptation strategy, which clarifies how projected climate and ocean acidification impacts should be incorporated into decision-making under the Species at Risk Act (SARA). Second is a legal course that includes an amendment of SARA or regulations thereunder that require up-to-date climate and ocean acidification information to be incorporated during recovery planning. In addition to the administrative and legal courses suggested, a precautionary, yet bold and flexible approach to recovery planning is advocated that aims to achieve species resilience rather than meeting historical population levels (which may already be impossible to achieve given shifting ecological, biological and physical baselines. This article is a follow up to a similar piece that examined Atlantic species at risk.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".