Climate change to drive increasing overlap between Pacific tuna fisheries and emerging deep-sea mining industry
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 In ocean areas beyond national jurisdiction, various legal regimes and governance structures result in diffused responsibility and create challenges for management. Here we show those challenges are set to expand with climate change driving increasing overlap between eastern Pacific tuna fisheries and the emerging industry of deep-sea mining. Climate models suggest that tuna distributions will shift in the coming decades. Within the Clarion-Clipperton Zone of the Pacific Ocean, a region containing 1.1 million km 2 of deep-sea mining exploration contracts, the total biomass for bigeye, skipjack, and yellowfin tuna species are forecasted to increase relative to today under two tested climate-change scenarios. Percentage increases are 10–11% for bigeye, 30–31% for skipjack, and 23% for yellowfin. The interactions between mining, fish populations, and climate change are complex and unknown. However, these projected increases in overlap indicate that the potential for conflict and resultant environmental and economic repercussions will be exacerbated in a climate-altered ocean. This has implications for the holistic and sustainable management of this area, with pathways suggested for closing these critical gaps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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