Building the Knowledge-to-Action Pipeline in North America: Connecting Ocean Acidification Research and Actionable Decision Support
Bibliographic record
Abstract
Ocean acidification (OA) describes the progressive decrease in the pH of seawater and other cascading chemical changes resulting from oceanic uptake of atmospheric carbon. These changes can have important implications for marine ecosystems, creating risk for commercial industries, subsistence communities, cultural practices, and recreation. Characterizing the extent of acidification and predicting the ramifications for marine and freshwater resources and ecosystem services are critical to national and international climate mitigation discussions and to local communities that rely on these resources. Based on critical grassroots connections between scientists and stakeholders, “Knowledge-to-Action” networks for ocean acidification issues have formed at regional international and global scales to take action. We review examples at these three levels where groups are elevating the issue of ocean acidification and developing practicable, implementable steps to mitigate causes, to adapt to unavoidable change, and to build resilience to changing ocean conditions in the marine environment and coastal communities. While these first steps represent critical efforts in protecting ecosystems and economies from the risks posed by ocean acidification, some challenges remain. Sensitivity and risk to OA varies by region and industry; priorities for action can vary between multiple and conflicting partners; evidence-based strategies for OA risk mitigation are still in the early stages; and there remain gaps between scientific research and actionable decision-maker support products. However, these scaled networks have proven to be adept at identifying and addressing these barriers to action. In the future, it will be critical to expand funding for food web impact studies, development of decision support tools, and to maintain the connections between scientists and marine resource users to build resilience to ocean acidification impacts.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".