Seasonal and Interannual Variations of Sea Temperature Influenced by Galápagos Islands in Eastern Tropical Pacific Ocean
Bibliographic record
Abstract
Abstract The sea surface temperature and surface currents during 1993–2016 based on satellite remote sensing, along with the ocean temperature and currents during 1993–2013 from a data assimilative ocean simulation product, are analyzed to investigate the influence of the Galápagos Islands (GI) in the eastern tropical Pacific on the sea surface temperature. Variations of the cold core west of the GI are quantified by TC − TW, TC − TE, and the cold core area (CCA), where TW, TC, and TE are the sea surface temperature to the west of the cold core, in the cold core, and to the east of the GI, respectively. TC − TE and the CCA show a stronger influence of the GI in boreal fall than in spring, in contrast to TC − TW, which shows a stronger influence in spring than in fall. All three indices show weaker (stronger) GI's influence during El Niño (La Niña) years, but only CCA and TC − TE have significant correlations of −0.66 and 0.61 with the Niño3 index. Variations of TC − TW are related to that of upwelling immediately the west of the GI. Variations of TC − TE and CCA are related to that of the spatial distributions of the upper ocean temperature, the advection of temperature by the horizontal currents, specifically the north branch of the South Equatorial Current, and upwelling in the eastern tropical Pacific.
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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 | 0.000 | 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 | 0.001 | 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".