Ocean acidification and marine aquaculture in North America: potential impacts and mitigation strategies
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 Shifting environmental conditions resulting from anthropogenic climate change have recently garnered much attention in the aquaculture industry; however, ocean acidification has received relatively little attention. Here, we provide an overview of ocean acidification in the context of North American aquaculture with respect to potential impacts and mitigation strategies. North American shellfish farms should make ocean acidification an immediate priority, as shellfish and other calcifying organisms are of highest concern in an increasingly acidifying ocean and negative effects have already been felt on the Pacific coast. While implications for various finfish have been documented, our current understanding of how acidification will impact North American finfish aquaculture is limited and requires more research. Although likely to benefit from increases in seawater CO 2 , some seaweeds may also be at risk under more acidic conditions, particularly calcifying species, as well as non‐calcifying ones residing in areas where CO 2 is not the primary driver of acidification. Strategies to mitigate and adapt to the effects of acidification exist on the regional scale and can aid in identifying areas of concern, detecting changes in seawater carbonate chemistry early enough to avoid catastrophic outcomes, and adapting to long‐term shifts in oceanic pH. Ultimately, ocean acidification has already imposed negative impacts on the aquaculture industry, but can be addressed with sufficient monitoring and the establishment of regional mitigation plans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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