Integrating equity-focused planning into coral bleaching management
Bibliographic record
Abstract
Abstract Coral bleaching, associated with warm water temperatures of the oceans, represents the most significant threat to coral reef ecosystems and coastal communities regarding climate change. Coral bleaching prediction models have emerged as essential tools in conservation and policy-making. However, the effectiveness of these models as an equity-focused science-policy nexus remains uncertain when local human community perspectives are disregarded. This paper presents an equity-focused framework for coral bleaching prediction and response, integrating local goals and contexts. We discuss the equity gaps during coral bleaching assessments while emphasizing the importance of early warning systems in promoting and facilitating more accurate reporting of bleaching episodes. Additionally, this research also highlights the complex but inherent interactions of multiple drivers, underscoring the need for cautious and socially inclusive strategies for climate adaptation. This perspective paper advocates for an equitable approach in science-policy networks to support the preservation of coral reefs while safeguarding the well-being of reef-related coastal communities.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".