A Selected Review of Impacts of Ocean Deoxygenation on Fish and Fisheries
Bibliographic record
Abstract
Oxygen is crucial for the survival of marine species. Yet, the ocean has experienced a loss of approximately 2% of its oxygen inventory since the last century, resulting in adverse impacts on marine life and ecosystems. In particular, changes in the gap between the supply and demand for dissolved oxygen lead to physiological and ecological variations, which cause alterations in habitats and food webs for fish and ecosystem services. These changes vary over time and by region, and the heterogeneous characteristics of marine species bring about non-linear consequences to human society. Despite this, identifying the potential ripple effects of deoxygenation on human society is challenging due to the integrated impacts of other stressors, such as global warming and ocean acidification, and their varying changes depending on environmental conditions and regions, such as upwelling and eutrophication. Therefore, we conducted a literature review on ocean deoxygenation and its effects on fish dynamics and the ecosystem, with a focus on the environmental and societal impact, to present crucial considerations and pathways for future research on ocean deoxygenation. We found that quantitative approaches are necessary to assess the dynamic changes under deoxygenation, and the consequent effects on marine ecosystems should be verified to exploit the natural resources from the ocean. One of the most reliable approaches to quantifying the ripple impacts of deoxygenation is to model spatial and temporal changes with other climate stressors, forming a global network encompassing socio-economic and regional effects of this global change to facilitate and improve capabilities to address the impacts of ocean deoxygenation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".