Long-Term Sea Surface Temperature Variability along the U.S. East Coast
Bibliographic record
Abstract
Abstract Sea surface temperature variations along the entire U.S. East Coast from 1875 to 2007 are characterized using a collection of historical observations from lighthouses and lightships combined with recent buoy and shore-based measurements. Long-term coastal temperature trends are warming in the Gulf of Maine [1.0° ± 0.3°C (100 yr)−1] and Middle Atlantic Bight [0.7° ± 0.3°C (100 yr)−1], whereas trends are weakly cooling or not significant in the South Atlantic Bight [−0.1° ± 0.3°C (100 yr)−1] and off Florida [−0.3° ± 0.2°C (100 yr)−1]. Over the last century, temperatures along the northeastern U.S. coast have warmed at a rate 1.8–2.5 times the regional atmospheric temperature trend but are comparable to warming rates for the Arctic and Labrador, the source of coastal ocean waters north of Cape Hatteras (36°N). South of Cape Hatteras, coastal ocean temperature trends match the regional atmospheric temperature trend. The observations and a simple model show that along-shelf transport, associated with the mean coastal current system running from Labrador to Cape Hatteras, is the mechanism controlling long-term temperature changes for this region and not the local air–sea exchange of heat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".