Modelling the Grand Banks commercial fishing fleet: Fleet structure, fishing performance and economic viability
Bibliographic record
Abstract
The Grand Banks commercial fishing industry has been faced with several crises in the past decades. These crises have included the major financial crunch and inflation of the late 1970s and early 1980s, as well as the resources collapse of the Northern cod stock and other groundfish stocks in the 1990s followed by the foreign fishing disputes of the mid 1990s. The thesis examines the evolution of the fishing industry in Atlantic Canada during these critical years with focus on the fisheries of the Grand Banks. A linear programming model of the configuration of the Grand Banks commercial fishing fleet is formulated to describe the post 2000 period. The model is driven using the results of an extensive analysis of historical records for this recent period. The model results are validated by comparing them with historical average annual data over the period 2000-2005. The linear programming model is run under several scenarios emulating changes in government policy and economic conditions affecting the harvesting sector. Based on the results, alternative fishing fleet configurations for the Grand Banks fishery are defined to improve the economic viability of the fishing fleet. The model pointed to changes in fleet configuration including a rationalization of the shrimp and crab fleets and a shift to longline vessels with higher-valued product for groundfish harvesting. Once implemented, these suggestions would advance the goals of the new "Oceans to Plate" approach to fisheries management recently announced by Fisheries and Oceans, Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".