Coral health monitoring: linking coral colour and remote sensing techniques
Bibliographic record
Abstract
Percent of living coral cover is an indicator commonly used to assess reef status. This study tested whether Coral Health Chart scores could be used as a proxy for spectral reflectance, which would provide a basis for mapping living coral cover at a finer scale (colour) using remote sensing techniques. A total of 1264 spectral reflectance measurements were taken in situ from corals representing the colour scores on the Coral Health Chart at Heron Island, Great Barrier Reef, Australia. Spectral analyses of reflectance magnitude showed that living coral could be classified with 72.41% overall accuracy into three colour categories: bleached, medium, and dark coral. First- and second-order derivative analyses did not improve the accuracy of classifying coral spectra into colour categories. Spectral analyses using only coral spectra from the genus Acropora also failed to improve classification results significantly, consistent with suggestions that coral reflectance is independent of taxonomy at the genus level. The results of this study provide a foundation for using the Coral Health Chart as a proxy to map and monitor living coral condition using remote sensing techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".