Investigating trade-offs in alternative catch share systems: an individual-based bio-economic model applied to the Bay of Biscay sole fishery
Bibliographic record
Abstract
An individual-based bio-economic model is presented and applied to the Bay of Biscay sole (Solea solea) fishery to investigate alternative quota management systems from a multicriteria perspective. For this study, the model integrates several institutional arrangements related to catch share management. The current French co-management system with nontransferability of quota is compared with an alternative individual transferable quota (ITQ) system in a context of transition to maximum sustainable yield. Trade-offs between ecological and socioeconomic impacts are highlighted and the effectiveness of governance scenarios is discussed in regard to the challenge of capacity adjustment. Results emphasize that the introduction of ITQs is expected to reduce by 40% the number of vessels in the fishery. While effectively mitigating the economic impacts of the transition phase to maximum sustainable yield, ITQs are also expected to increase substantially the fishing effort by trawlers, which may cause ecological concerns. The scenarios tested also include the simulation of a decommissioning scheme where subsequent decommissioned vessels are considerably different from the vessels that would lease out their quotas in an ITQ system, resulting in differentiated ecological and socioeconomic impacts between scenarios.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".