Impacts of a regulatory boundary on effort distributions in the Northwest Atlantic Fisheries Organization 4X and 5Z groundfish fisheries
Bibliographic record
Abstract
Effort distributions in commercial fisheries have previously been found to follow an ideal free distribution. I studied the distribution of fishing vessels in various clusters of trawling effort in the Northwest Atlantic Fisheries Organization (NAFO) Divisions 4X and 5Z, consisting of the Scotian Shelf and Georges Bank, to determine if effort distributions across a regulatory boundary follow the predictions of an ideal free distribution. First, a generalized linear mixed model was used to study the effects that impact value per unit effort (VPUE) in this fishery. Year, vessel length overall, and area fished were the fixed effects with an impact on VPUE with vessel ID and trip ID as the random effects of the model. Year coefficients often corresponded to estimated biomass for the corresponding year. VPUE tended to be higher along the boundary in 5Z. The next step used standardized value to generate isodars, equations that represent a line of equal fitness between areas, that predicted the distribution of effort among the different areas. Focusing on isodars involving areas across the 4X-5Z boundary and isodars involving the areas directly bordering the boundary allows for direct interpretation of the question of whether isodars are maintained across the regulatory boundary. Isodars were able to predict effort near the boundary and across the boundary in most situations. These findings support the overall hypothesis that effort distribution around a regulatory boundary resembles an ideal free distribution. However, the inaccuracy of predicted effort in areas of high density emphasizes the need to further study areas where the assumptions of ideal free distributions are not met.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".