Research requirements of an ecosystem approach to fisheries.
Bibliographic record
Abstract
In recent years, there has been considerable discussion within the international scientific and ocean policy community on an ecosystem approach to the integrated management (IM) of multiple ocean uses, and specifically its application to fisheries. Much of this discussion has focused on the planning hierarchy composed of high-level conceptual objectives and ocean industry-level operational objectives with associated indicators and reference points. There has also been discussion on governance systems including planning area considerations and the engagement of stakeholders in the management process. While a lot has been learned from these efforts, it is timely to consider the scientific research requirements of effective implementation. This chapter presents a methodology for defining the research needs of an ecosystem approach to fisheries (EAF) based upon experience on Canada's East Coast. The methodology is applied to the IM of the Eastern Scotian Shelf (ESS), which is a regional pilot initiative in Canada. The research issues identified focus on interactions between fisheries and the environment, as well as those required to evaluate cumulative impacts. Attention is also given to the large-scale influences of climatic and oceanographic systems on ecosystems and their dependent fisheries.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".