Marine capture fisheries in the Arctic: winners or losers under climate change and ocean acidification?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change, ocean acidification (OA) and the subsequent changes in marine productivity may affect fisheries and eventually the whole economy in the Arctic. We analysed how changes in climate and ocean pH under scenarios of anthropogenic CO 2 emissions are likely to affect the economics of marine fisheries in the Arctic. We applied a Dynamic Bioclimate Envelope Model (DBEM) and outputs from four different Earth System Models (ESMs) to project future changes in the distribution and maximum catch potential of exploited marine fishes and invertebrates. We projected that total fisheries revenue in the Arctic region may increase by 39% (14–59%) by 2050 relative to 2000 under the Special Reports on Emission Scenario (SRES) A2. Simultaneously, total fishing costs, fishers’ incomes, household incomes and economy‐wide impacts in the Arctic are also projected to increase. Climate change with OA is expected to reduce the potential increases in catch and the economic indicators studied herein. Although the projections suggest that Arctic countries are likely to be ‘winners’ under climate change in comparison with tropical developing countries, the effects of OA will lower the expected future benefits in the Arctic. The predicted impacts are likely to be conservative as we consider only the direct effects of OA on fishes and calcifiers, of which there are only a few in the Arctic. Results of this study would be useful for designing effective adaptation strategies to climate change and measures to mitigate the potential negative impacts of OA in the Arctic.
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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 | 0.000 |
| 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) | 0.000 | 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 it