Application of the Precautionary Approach to the Management of Marine Mammals in northern Canada
Bibliographic record
Abstract
Canada is committed to managing its resources using a Precautionary Approach (PA). However, when applying this approach to Arctic marine mammals, the Government of Canada must also respect the land claims agreements it has signed with Canada’s Inuit. Under these agreements the co-management boards are responsible for wildlife management within the land claim area. In addition to protecting the rights of hunters to harvest, the land claims agreements also call for the development of management systems that respect the principles of conservation and ensure sustainability of the resource, potentially resulting in a management paradox. We present criteria by which the status of a population can be assessed, and an appropriate PA framework applied. If sufficient data are available to understand the population dynamics of a given stock (i.e., a Data Rich situation), management decisions can be based upon an appropriate population model with quantitatively estimated reference levels. In cases where the population dynamics are poorly understood (i.e., Data Poor), a more conservative approach, referred to as the Potential Biological Removal (PBR) should be used to provide advice on sustainable harvest levels. Generally, only the most recent estimate of abundance is used in the PBR calculation which may ignore other data. We propose that if sufficient data are available to fit a population model, while still not sufficient to be considered Data Rich, the modelled estimate of current abundance can be used for a more robust PBR estimate. We also review guidelines for the choice of the recovery factor which is part of the PBR calculation. The apparent management paradox can be addressed within the context of a Management Procedure or Management Strategy Evaluation where Indigenous Knowledge and Western Science can contribute to setting management objectives, decision rules and appropriate time-frames that can be evaluated within a simulation environment.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".