Bibliographic record
Abstract
We examined associations between warm sea surface temperature (SST) anomalies and coral bleaching in the Galapagos Islands and the Gulf of Panama, in the tropical eastern Pacific Ocean. Interannual SST variability is dominated by the El Nino-Southern Oscillation phenomenon at Galapagos, whereas only strong events have an SST signature in Panama. We explored various SST-related metrics potentially associated with bleaching occurrence: maximum absolute SST, SST anomaly, and the combined effect of intensity and duration of both SST anomalies (described via a “degree days” index) and high SST events. In Galapagos, three Nino years (1983, 1987, and 1992) coincided with bleaching. These were the top three years in both maximum annual SSTs and degree days values. In Panama, bleaching in 1983 coincided with high maximum SSTs and high degree days. In contrast, no bleaching was detected in 1972 despite high values of both quantities. We found all temperature-related metrics to be highly correlated, and it was impossible to isolate their effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.987 | 0.987 |
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; the direct Gemma label and the distilled Codex classifier 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".