Transboundary impacts of fishing activities along the northeast continental shelf
Bibliographic record
Abstract
In Manuscript I, I construct a stylized conceptual model to evaluate the international trade of the Atlantic groundfish fishery between the US and Canada. The model incorporates both the biological and economic components of the fishery, and explores the environmental, economic and some welfare implications through trade. First I explore the consequence of each country imposing a different fishery management regime. Second, I incorporate the spatial dynamics of the stock into this framework. Third, I use the conceptual model to discover the effects this may have on international trade. And finally I include the spatial dynamics of the stock into the model of international trade. In Manuscript II, I investigate the prospects for cooperative management of the transboundary groundfish fishery of Georges Bank. I estimate the change in benefits to competing harvesters that results from adjusting current management policies, and examine the change in these benefits by using a stylized model that attempts to capture essential spatial aspects of the Georges Bank groundfish stock, as well as the particular management preferences of the competing harvesters, the US and Canada. The numerical simulations demonstrate that complementary management can enhance stock size, harvest and economic returns; and that there may be win-win solutions where both counties are better off. Manuscript III, presents an empirical model of Georges Bank transboundary groundfish fishery, and reports results of a dynamic bioeconomic simulation used to evaluate the consequences of alternative management strategies for the multispecies fishery. The biological component of the model describes the population and spatial dynamics of the principle groundfish stocks that reflect the seasonal migration of the transboundary stocks. The economic component incorporates the harvest strategies of the US otter trawl fleet and the Canadian longline and otter trawl fleets. For each management alternative evaluated, the model simulates the change in profits for both countries jointly, as well as each country individually. I evaluate alternative fisheries management strategies to determine whether the current management is superior to any other strategy and, if not, what other strategies are better in terms of economic performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".