Marine capture fisheries in the Arctic: winners or losers under climate change and ocean acidification?
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".