Insights From a Decade of Research on Coral Reef—Atmosphere Energetics
Bibliographic record
Abstract
Abstract Coral reefs cover approximately 0.10 to 0.25% of the marine environment and yet are home to around 25% of marine species and support the livelihoods of more than 500 million people. They face a wide range of threats, with the impact of global warming gaining most attention due to its frequently claimed causal link to coral bleaching. Here we review a decade of research into the micrometeorology of Heron Reef, a lagoonal platform coral reef in the southern Great Barrier Reef, Australia. Using novel pontoon‐mounted eddy covariance systems, we show that often >80% of net radiation is partitioned into heating the water overlying the reef, the reef benthos, and substrate. Significant spatial variability in energy and trace gas exchanges occurs over the reef in response to different geomorphic and hydrodynamic conditions. Synoptic weather patterns that bring light winds, clear skies, and high humidity, result in reef scale meteorology that appears to have a greater influence on coral bleaching events than the background oceanic warming trend. The reef develops its own convective internal boundary layer, with potential to influence cloud development and therefore the surface energy balance. Knowledge of such local effects is lacking, so it is recommended that future research is needed into reef scale processes and how they interact with larger‐scale forcing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".