Predicting cold-water bleaching in corals: role of temperature, and potential integration of light exposure
Bibliographic record
Abstract
Warm-water growth and survival of corals are constrained by a set of environmental conditions such as temperature, light, nutrient levels and salinity. Water temperatures of 1 to 2°C above the usual summer maximum can trigger a phenomenon known as coral bleaching, whereby disruption of the symbiosis between coral and dinoflagellate micro-algae, living within the coral tissue, reveals the white skeleton of coral. Anomalously cold water can also lead to coral bleaching but has been the subject of limited research. Although cold-water bleaching events are less common, they can produce similar impacts on coral reefs as warm-water events. In this study, we explored the effect of temperature and light on the likelihood of cold-water coral bleaching from 1998-2017 using available bleaching observations from the Eastern Tropical Pacific and the Florida Keys. Using satellite-derived sea surface temperature, photosynthetically available radiation and light attenuation data, cold temperature and light exposure metrics were developed and then tested against the bleaching observations using logistic regression. The results show that cold-water bleaching can be best predicted with an accumulated cold-temperature metric, i.e. ‘degree cooling weeks’, analogous to the heat stress metric ‘degree heating weeks’, with high accuracy (90%) and fewer Type I and Type II errors in comparison with other models. Although light, when also considered, improved prediction accuracy, we found that the most reliable framework for cold-water bleaching prediction may be based solely on cold-temperature exposure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".