Impact of Ocean Warming, Overfishing and Mercury on European Fisheries: A Risk Assessment and Policy Solution Framework
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
Previous studies have shown that multiple-environmental stressors are expected to have significant and geographically differential impacts on the health and abundance of marine species. In this paper, we analyze the combined impacts of ocean warming, overfishing and mercury pollution in European waters by projecting the impacts of climatic and non-climate drivers on marine species in European waters. Our findings suggest that the impacts vary widely depending on different species and their mean temperature tolerance (MTT). We find for instance, that more than 5 temperate benthopelagic species including, bobtail squids ( Sepiida) frogfishes ( Lophius) great Atlantic scallop ( Pecten maximus) red mullet ( Mullus barbatus barbatus) and common octopus ( Octopus vulgaris) are affected (i.e., weakens their resilience to climate change) by the increase in sea surface temperature (SST) under RCP 8.5 in 2050 and 2100. Mercury contamination was estimated to increase in some species (e.g., ∼50% in swordfish), exceeding mercury consumption guideline thresholds (>1 mg/kg). This negative impact may limit the capacity of fisheries and marine ecosystem to respond to the current climate induced pollution sensitivity. An implication of our study is that the international community should strengthen a global ban on mercury emissions under the mandate of the Minamata Convention, comparable to the United Nations framework for persistent organic pollutant emission sources. Ongoing global efforts aimed at minimizing carbon footprint and mercury emissions need to be enhanced in concert with a reduction in fishing intensity to maintain effective conservation measures that promote increased resilience of fisheries to climate change and other stressors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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