Historical Temperature Variability Affects Coral Response to Heat Stress
Bibliographic record
Abstract
Coral bleaching is the breakdown of symbiosis between coral animal hosts and their dinoflagellate algae symbionts in response to environmental stress. On large spatial scales, heat stress is the most common factor causing bleaching, which is predicted to increase in frequency and severity as the climate warms. There is evidence that the temperature threshold at which bleaching occurs varies with local environmental conditions and background climate conditions. We investigated the influence of past temperature variability on coral susceptibility to bleaching, using the natural gradient in peak temperature variability in the Gilbert Islands, Republic of Kiribati. The spatial pattern in skeletal growth rates and partial mortality scars found in massive Porites sp. across the central and northern islands suggests that corals subject to larger year-to-year fluctuations in maximum ocean temperature were more resistant to a 2004 warm-water event. In addition, a subsequent 2009 warm event had a disproportionately larger impact on those corals from the island with lower historical heat stress, as indicated by lower concentrations of triacylglycerol, a lipid utilized for energy, as well as thinner tissue in those corals. This study indicates that coral reefs in locations with more frequent warm events may be more resilient to future warming, and protection measures may be more effective in these regions.
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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 | 0.000 | 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) | 0.001 | 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".