Ocean acidification and marine aquaculture in North America: potential impacts and mitigation strategies
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 CO2, some seaweeds may also be at risk under more acidic conditions, particularly calcifying species, as well as non‐calcifying ones residing in areas where CO2 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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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".