Socio-economic tools to mitigate the impacts of ocean acidification on economies and communities reliant on coral reefs — a framework for prioritization
Bibliographic record
Abstract
Coral reef preservation is a challenge for the whole of humanity, not just for the estimated three billion people that directly depend upon coral reefs for their livelihoods and food security. Ocean acidification combined with rising sea surface temperatures, and an array of other anthropogenic influences such as pollution, sedimentation, over fishing, and coral mining represent the key threats currently facing coral reef survival. Here we summarize a list of agreements, policies, and socio-economic tools and instruments that can be used by global, national and local decision-makers to address ocean acidification and associated threats, as identified during an expert workshop in October 2017. We then discuss these tools and instruments at a global level and identify the key tasks for raising decision makers’ awareness. Finally, we suggest ways of prioritizing between different actions or tools for mitigation and adaptation.
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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 | 0.031 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".