Barriers to implementation of dynamic reference points in fisheries management
Bibliographic record
Abstract
Fish populations are dynamic; their productivity depends on the environment, predator and prey interactions, and fisheries harvest rates. Failure to account for these factors in fisheries science and management can lead to a misestimation of stock dynamics and productivity, resulting in overexploitation or forgone fisheries yield. Using an online survey, we asked fisheries scientists, industry stakeholders, Indigenous partners, and non-governmental organizations whether changing ecosystem productivity was a problem in their experience, how often dynamic approaches to fisheries reference points have been adopted, what methods had been used, and what fisheries they had been applied to. Changing fisheries or ecosystem productivity was reported as an issue by 96% of respondents; however, 74% of respondents said they had never seen dynamic reference points implemented, 16% said in very few instances, while 10% said frequently. The most common barriers to implementation of dynamic approaches in fisheries management were institutional inertia and uncertainty about whether a change in productivity was lasting. We discuss trade-offs between fisheries management performance and stability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".