The Role of Coral Reef Small-Scale Fisheries for Addressing Malnutrition and Avoiding Biodiversity Loss: A Vibrant Oceans Initiative Whitepaper
Bibliographic record
Abstract
Integrated management of coral reef foods, as a highly diverse set of blue foods, can contribute to addressing the dual challenges of malnutrition and biodiversity loss.Advances in nutrition research have made it possible to understand nutritional benefits on a species by species basis, and to make comparisons with benefits derived from land-based foods.We provide a series of considerations about current understanding of nutrition from coral reef foods, including the predominance of finfish in nutritional assessments, the importance of contaminants for food safety, uncertainty stemming from climate and cumulative impacts, and the need for locallyspecific assessments of food systems.Next we outline how nutrition, coral reef small-scale fisheries, and communities intersect.Aspects of equity and food sovereignty are reviewed as a basis for contextualizing current scientific understanding of nutrition while acknowledging who is actually benefiting nutritionally and materially from coral reef fisheries.Given this understanding of the state of knowledge of nutrition from coral reef foods, we encourage the development of nutrition-sensitive coral reef governance.We conclude with a set of recommendations for governance institutions, fishing organizations, philanthropic foundations, funding agencies, conservation organizations, and researchers, among others.To ensure coherence, we encourage these stakeholders to work with each other and with communities for implementation of the following recommendations:The Role of Coral Reef Small-Scale Fisheries for Addressing Malnutrition and Avoiding Biodiversity Loss
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".