Transitional states in marine fisheries: adapting to predicted global change
Bibliographic record
Abstract
Global climate change has the potential to substantially alter the production and community structure of marine fisheries and modify the ongoing impacts of fishing. Fish community composition is already changing in some tropical, temperate and polar ecosystems, where local combinations of warming trends and higher environmental variation anticipate the changes likely to occur more widely over coming decades. Using case studies from the Western Indian Ocean, the North Sea and the Bering Sea, we contextualize the direct and indirect effects of climate change on production and biodiversity and, in turn, on the social and economic aspects of marine fisheries. Climate warming is expected to lead to (i) yield and species losses in tropical reef fisheries, driven primarily by habitat loss; (ii) community turnover in temperate fisheries, owing to the arrival and increasing dominance of warm-water species as well as the reduced dominance and departure of cold-water species; and (iii) increased diversity and yield in Arctic fisheries, arising from invasions of southern species and increased primary production resulting from ice-free summer conditions. How societies deal with such changes will depend largely on their capacity to adapt--to plan and implement effective responses to change--a process heavily influenced by social, economic, political and cultural conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".