Correlation Between Coral Lesions and Skin Hyperpigmentation in Reef Fish on the Southwest Coast of Grenada, West Indies
Bibliographic record
Abstract
Marine ecosystems rely on hard corals. Since 2014, a rapidly spreading disease causing stony coral tissue loss disease (SCTLD) has devastated coral reefs in the Caribbean. In 2019, corals exhibiting necrotic lesions compatible with SCTLD were documented on the coast of Grenada in the West Indies. These lesions, hereafter called Stony Coral Necrotic Lesions (SCNL), are associated with coral death. Concomitantly on the same reefs, signs of skin hyperpigmentation were detected in French grunt (Haemulon flavolineatum) and ocean surgeonfish (Acanthurus bahianus). This field study investigated potential correlations between SCNL abundance in representative transects and fish hyperpigmentation across 12 dive sites on Grenada's Southwest coast. The percentage of corals displaying SCNL was 45% of hard coral colonies. The study found a significant correlation (P = 0.004) between the percentage of fish affected by hyperpigmentation in a given reef and the abundance of SCNL in corals of the same dive site on evaluated transects. Sites located in St-Georges Bay also tended to have a higher proportion of diseased corals, but no significant difference was noted between study regions. This preliminary study provides insights into SCNL in Grenada and establishes a foundation for future longitudinal investigations including further evaluation of human-induced stressors that may threaten coral and fish health and make them more susceptible to diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".