Nonlinear Sea-Level Trends and Long-Term Variability on Western European Coasts
Bibliographic record
Abstract
Ezer, T.; Haigh, I.D., and Woodworth, P.L., 2016. Nonlinear sea-level trends and long-term variability on western European coasts.Nonlinear trends and long-term variability in sea level measured on the U.K. and western European coasts with long tide-gauge records (∼100–200 y) were investigated. Two different analysis methods, a standard quadratic regression and a nonparametric, empirical mode decomposition method, detected similar positive sea-level accelerations during the past ∼150 years: 0.014 ± 0.003 and 0.012 ± 0.004 mm/y2, respectively; these values are close to the sea-level acceleration of the global ocean over the same period, as reported by several studies. Ensemble calculations with added white noise are used to evaluate the robustness of low-frequency oscillations and to estimate potential errors. Sensitivity experiments evaluate the impact of data gaps on the ability of the analysis to detect decadal variations and acceleration in sea level. The long-term oscillations have typical periods of 15–60 years and ranges of 50–80 mm; these oscillations appear to be influenced by the North Atlantic Oscillation and by the Atlantic Multidecadal Oscillation. Analysis of altimeter data over the entire North Atlantic Ocean shows that the highest impact of the North Atlantic Oscillation is on sea-level variability in the North Sea and the Norwegian coasts, whereas the Atlantic Multidecadal Oscillation has the largest correlation with sea level in the subpolar gyre and the Labrador Sea, west of the study area.
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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 | 0.001 |
| 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".