Review of the ecosystem approach in Cumberland Sound, Nunavut, Canada
Bibliographic record
Abstract
Historically, fisheries have been monitored at the individual stock level, without consideration to connectivity to other species or activities in the ecosystem. The ecosystem approach requires that the stock and fishery be seen in the context of predators, competitors, prey, by-catch impacts, other fisheries, and abiotic environmental variables so that management is holistic. In this study, we describe the components of the ecosystem approach applied in the scientific investigation of fisheries in Cumberland Sound, Nunavut. Relative to other Canadian Arctic locales with commercial fisheries operations, the Cumberland Sound area has a greater biodiversity and abundance of fishes and marine mammal species. These components support active fisheries for Arctic Charr, Greenland Halibut, and Beluga Whale, as well as Ringed, Bearded, and Harp Seals. The species and their fisheries are variable in character, their ecosystem effects, and their response to the environment. We describe the species dynamics and their fisheries within an ecosystem context. We briefly note the challenges to developing an overarching model of the system such as the integration of the different life histories of the species, as well as the incorporation of future non-fisheries-related disturbances.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".