Bibliographic record
Abstract
Coral reefs present a multitude of ecosystem services and benefits, but these ecosystems are becoming increasingly threatened. Internationally, coral reefs are facing a multitude of challenges with many of these deriving from or induced by human activities, and this is evident in the Caribbean. Commonly cited impacts include climate change, pollution, development, tourism, and overfishing, while less discussed but also important are marine debris and the ornamental trade. With the rise of restoration initiatives to mitigate coral reef losses, initiatives should present diverse approaches and account for complexity to mimic the intricacy of natural coral reef systems; facilitate stronger management and governance practices; and integrate a focus on novel coral ecosystems. A survey study is conducted of restoration projects located around the Caribbean Sea to apply the literature to practical examples, and outline which restoration approaches are being used, the most common human impacts that coral reefs are facing in the area, and the challenges projects are facing. 11 projects (and 12 individuals) from different locations were surveyed and quantified to depict common trends. Results outline that the majority of restoration projects present diverse, active approaches that are being implemented and or considered. There are improvements that can be made in some areas; however, considering the challenges, complexity and economic strains behind coral restoration, survey results show that achieving multi-faceted approaches requires many non-linear factors with some of the variables being beyond the control of restoration projects themselves. Ultimately, it is necessary that local governments and global networks place a stronger focus on assisting restoration projects with updating regulations and frameworks in regard to human activities, establishing standardized guidelines for restoration, and improving economic support for restoration initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".