Can green hydrogen production be used to mitigate ocean deoxygenation? A scenario from the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract Ocean deoxygenation and expansion and intensification of hypoxia in the ocean are a major, growing threat to marine ecosystems. Measures currently used to protect marine biodiversity (e.g., marine protected areas) are ineffective in countering this threat. Here, we highlight the example of the Gulf of St. Lawrence in eastern Canada, where oxygen loss is not only due to eutrophication (which can be mitigated by nutrient controls) but also is a consequence of ocean circulation change and warming. Climate-related loss of oxygen will be an increasingly widespread source of risk to marine biodiversity over this century. Again using the Gulf of St. Lawrence as an example, we show that production of oxygen by the green hydrogen industry can be comparable to the loss rate of dissolved oxygen on large spatial scales, offering new possibilities for mitigation. However, this mitigation approach has rarely been considered for marine environments to date. Given confluence of increasing risk to marine ecosystems from oxygen loss and rapid emergence, worldwide, of industrial sources of pure oxygen, which are likely to be located in coastal regions, we believe this option will be proposed increasingly in coming years, including by the private sector. We argue that it is urgent for ocean scientists, engineers, and policymakers to recognize and address this emerging potential. A coordinated research effort should be established immediately in order to harness the potential of the green hydrogen industry to mitigate major impacts of climate change on marine biodiversity, and avoid any unintended negative consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".