Higher spatial resolution is not always better: evaluating satellite-sensed sea surface temperature products for a west Pacific coral reef system
Bibliographic record
Abstract
As marine heatwaves and mass coral bleaching events rise in frequency and severity, there is an increasing need for high-resolution satellite products that accurately predict reef thermal environments over large spatio-temporal scales. Deciding which global sea surface temperature (SST) dataset to use for research or management depends in part on the desired spatial resolution. Here, we evaluate two SST datasets - the lower-resolution CoralTemp v3.1 (0.05° ~ 5 km grid) and the Multiscale Ultra-high Resolution MUR v4.1 (0.01° ~ 1 km grid) - in their ability to predict in situ reef thermal environments (nightly mean and daily maximum) and the severity of past bleaching in Palau, western Pacific Ocean. We expected higher-resolution data to improve prediction accuracy, yet CoralTemp data explained 10% additional variability of in situ temperatures and 70% additional variance in past bleaching. This likely relates to differential data protocols; despite MUR using a higher spatial resolution grid, CoralTemp achieves higher raw satellite observation density in coastal areas by utilising geostationary satellites. MUR SSTs were also consistently more variable. These results reinforce calls to develop more accurate high-resolution SST products for coral reefs. Our paper demonstrates that higher spatial resolution SST data is not necessarily better at predicting in situ thermal environments of coral reefs and local marine heatwave impacts.
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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 | 0.005 | 0.009 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".