Fisheries in flux: Bridging science and policy for climate-resilient management of US fisheries under distributional change
Bibliographic record
Abstract
As climate change reshapes marine ecosystems, the dynamics of fish stocks are undergoing rapid transformation. Understanding these shifts and their multifaceted impacts demands more than just scientific inquiry; it necessitates a fusion of knowledge, collaboration, and action. However, the translation of cutting-edge research on the changing distributions and abundance of fish stocks into actionable strategies remains a daunting challenge. Climate change considerations are a relatively new area for fisheries management in the US, and there is often a gap between the scientific research being produced and the management processes through which it can be applied in practice. To address this gap, this research utilizes a co-productive workshop approach to elucidate and assess the current trajectory from scientific inquiry to management practice in the context of climate-impacted US fisheries. The workshop and subsequent analyses yielded 27 actionable recommendations and two strategic pathways. These pathways were designed to concentrate efforts on two critical fronts: 1) enhancing venues for collaboration between scientists and managers; and 2) establishing a cooperative framework for defining and prioritizing goals for climate-resilient management. Post-hoc analyses grounded these pathways within established frameworks and literature related to implementation science and science-policy connectivity. Tangible examples further exemplify the recommended actions and demonstrate the practical significance of this work for enhancing resilient management of fisheries in the face of climate uncertainty.
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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 | 0.040 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".