Statistical Modes and Physical Drivers of Multidecadal Sea Surface Temperature Variability in the Northwest Atlantic
Bibliographic record
Abstract
Variations in ocean temperature and the presence of large multidecadal sea surface temperature (SST) oscillation can have a severe impact on Northwest Atlantic climate, ecosystems and fisheries. Differences between historical SST datasets in the regions have also been consistently noted. When determining patterns of variability in Northwest Atlantic SST, steps should be taken to consider cross-dataset variability. Here, a novel combined-dataset approach from 1901 to 2010 was used along with an extended empirical orthogonal function (EEOF) analysis to determine the leading modes of variability over the Northwest Atlantic shelf and slope. Second, a mixed-layer heat budget from 1850 to 2015 was used to determine the dominant physical processes driving yearly-to-multidecadal variability across the Northwest Atlantic shelf and slope. Results from the EEOF suggest that positive Atlantic Multidecadal Oscillation (AMO) years are driven by positive North Atlantic Oscillation years (NAO).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".