How quota cuts, recreational fishing, and predator conservation can shape coastal commercial fishery efforts
Bibliographic record
Abstract
Commercial fishing effort often collides with other uses and interests. Fisheries resources might be co-used by recreational fishers (anglers), support populations of non-human top-predators, and fishing space might be preserved for conservation purposes. This can affect how commercial fisheries operate. We studied how the spatial distribution of commercial fishing effort among fishing districts is related to quotas, the use of resources by anglers, and the re-emergence of natural predators (the grey seal (Halichoerus grypus) and the great cormorant (Phalacrocorax carbo sinensis)). Our study area is the small-scale commercial fishery of Mecklenburg-Vorpommern in Germany. We use seemingly unrelated regressions with vessel-level time series of commercial targeting behavior. Spatial shifts in effort shares were related to larger quotas. Further, effort would shift away from areas with high seal densities, but no clear changes with respect to cormorants were found. The relationship between commercial effort share and angling activity was mixed and depended on the type of angler. We found a negative relationship between commercial fishing activity and angling in fishing areas where fishing for trophy pike (Esox lucius) and other predator species was popular.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".