A Call for Evidence-Based Conservation and Management of Fisheries and Aquatic Resources
Bibliographic record
Abstract
Abstract Natural resource management agencies implement conservation policies with the presumption that they are effective and of benefit to aquatic ecosystems. However, it is often difficult to decide what management action to implement and what will be most effective. Here we call for natural resource management agencies to fully adopt and implement evidence-based management (EBM) for conservation and fisheries management. We support this call by providing a primer on systematic reviews, a core tool in evidence synthesis but one that is rarely used in the context of fisheries management. We highlight the benefits and challenges associated with implementing EBM, with a particular focus on the routine decisions and management actions undertaken by natural resource practitioners. We submit that by adopting EBM, practitioners would have access to the best available evidence on the effectiveness of various management and conservation interventions, while providing defensible and credible evidence to inform decision-making processes and policies.
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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 | 0.467 | 0.646 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.030 | 0.051 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.048 | 0.049 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".