Ocean acidification research for sustainability: co-designing global action on local scales
Bibliographic record
Abstract
Abstract The global threat that ocean acidification poses to marine ecosystems has been recognized by the UN 2030 Agenda under Sustainable Development Goal, Target 14.3: to reduce ocean acidification. The Global Ocean Acidification Observing Network (GOA-ON) is a collaborative international network to detect and understand the drivers of ocean acidification in estuarine-coastal-open ocean environments, the resulting impacts on marine ecosystems, and to make the information available to optimize modelling studies. The Ocean Acidification Research for Sustainability (OARS) programme, endorsed by the 2021–2030 UN Decade of Ocean Science for Sustainable Development, will build on the work of GOA-ON through its seven Decade Action Outcomes. By employing a Theory of Change framework, and with the co-design of science in mind, OARS will develop an implementation plan for each Decade Action Outcome, which will identify the stakeholders and rights-holders, as well as the tools, means, and positive consequences required for their successful delivery. The organizational structure of GOA-ON, with nine regional hubs, will benefit OARS by providing a vital connection between local and global scales. GOA-ON regional hub case-studies illustrate how activities in the past and future, informed by global and regional priorities, support capacity building and the co-design of ocean acidification science.
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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 | 0.096 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".