Bibliographic record
Abstract
Abstract The four general components of an ecosystem approach to fisheries (EAF) are reviewed. In taking account of environment forcing in stock dynamics, arguments are presented that effects of environmental forcing on growth, maturation and natural mortality are often more important to management than effects on recruitment. In holding fisheries accountable for the ecosystem effects of fishing, it is argued that direct effects of fishing are generally known and can be managed. However, interactions among fisheries and between fisheries and other sectors pose difficult challenges to equitable decisions in managing these impacts, and many traditional incentives function differently in EAF than in target‐stock management. Achieving inclusiveness in decision‐making and stewardship is also made more complex in EAF, because of the much larger number of interests with a legitimate role in decision‐making. As a result, integrated management (IM) becomes a necessary component of EAF, although EAF and IM are not interchangeable concepts. The treatment of all four components of an EAF considers the need for a balanced and stable outcome on all three dimensions of sustainability – ecological, economic and social. It also highlights that different participant groups in governance display different risk tolerances for misses (not taking conservation action when needed) and false alarms (restraining access to social or economic benefits when little ecological benefit results). These differences in tolerances for different kinds of management errors often complicate decision‐making an EAF setting and raise transaction costs greatly.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".