Higher thermal resistance of corals in the global marine biodiversity center
Bibliographic record
Abstract
Predictions for the future of coral reef are largely based on thermal exposure and poorly account for geographic variation in biological sensitivity and resistance to thermal stress. Based on the ratio of thermal exposure and sensitivity, geographic variability of coral resistance was estimated during the 2016 global-bleaching event. Exposure was estimated as historical cumulative excess summer heat (CTA) and a multivariate index of SST, light, and water flow (CE). Site sensitivity was estimated for 226 sites using coordinated bleaching observations. Site resistance was evaluated by 128 possible models for the influences of geography, historical SST variation, coral cover, and number of coral genera. Most factors were statistically significant but the strongest factor was geography - Coral Triangle having higher resistance than non-Coral Triangle sites. Consequently, future predictions of thermal stress will need to account for strong geographic differences in acclimation/adaptation.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".