Where the waters meet: sharing ideas and experiences between inland and marine realms to promote sustainable fisheries management
Bibliographic record
Abstract
Although inland and marine environments, their fisheries, fishery managers, and the realm-specific management approaches are often different, there are a surprising number of similarities that frequently go unrecognized. We contend that there is much to be gained by greater cross-fertilization and exchange of ideas and strategies between realms and the people who manage them. The purpose of this paper is to provide examples of the potential or demonstrated benefits of working across aquatic boundaries for enhanced sustainable management of the world’s fisheries resources. Examples include the need to (1) engage in habitat management and protection as the foundation for fisheries, (2) rethink institutional arrangements and management for open-access fisheries systems, (3) establish “reference points” and harvest control rules, (4) engage in integrated management approaches, (5) reap conservation benefits from the link to fish as food, and (6) reframe conservation and management of fish to better engage the public and industry. Cross-fertilization and knowledge transfer between realms could be realized using environment-independent curricula and symposia, joint scientific advisory councils for management, integrated development projects, and cross-realm policy dialogue. Given the interdependence of marine and inland fisheries, promoting discussion between the realms has the potential to promote meaningful advances in managing global fisheries.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.024 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".