Bibliographic record
Abstract
Abstract Many fisheries management systems, even when based on apparently sound science, have failed to prevent severe overfishing. And even when successful in this sense, such systems have frequently resulted in a large degree of excess fishing capacity. The reason for these failures can often be found in a lack of consideration of the economic incentives affecting fishermen. Specifically, when forced to compete for a fixed total annual catch quota (TAC), fishermen are motivated to fish at high intensity, and to expand the fishing power of their vessels. Individual fishing quotas (IFQs) are being increasingly used as a method of altering economic incentives in a desirable way. IFQ systems, however, can also suffer severe shortcomings, unless substantial fees are extracted for the exclusive right to exploit a publicly owned resource. When combined with appropriate fees, or royalties, IFQs can indeed result in sustainable, profitable fisheries. There still remains the fundamental question of risk management, but this is also now beginning to be addressed. Thus there is now a strong hope for the future success of marine fisheries, at least within 200‐mile coastal zones.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".