Tilt of mean sea level along the <scp>P</scp>acific coasts of <scp>N</scp>orth <scp>A</scp>merica and <scp>J</scp>apan
Bibliographic record
Abstract
Abstract The tilt of coastal mean sea level with respect to an equipotential surface is estimated using two fundamentally different approaches. The geodetic approach is based on tide gauge and GPS observations, and a model of the geoid. The ocean approach uses a high‐resolution, dynamically based ocean model to estimate mean dynamic topography. Along the Pacific coast of North America the two approaches give similar large‐scale profiles with a minimum at about 40°N and a maximum in the northern part of the Gulf of Alaska. Along the Pacific coast of Japan the geodetically determined coastal sea levels indicate an eastward drop of about 20 cm along the south coast and a further northward drop across Tsugaru Strait. Both of these features are reproduced by the ocean models. An analysis of the alongshore momentum balance suggests that alongshore wind stress acting over the inner shelf is the primary driver of the mean sea level profile along the coast of North America. Several large‐scale features are explained using arrested topographic wave theory. A similar momentum analysis, and an additional study of time variability of sea level and circulation, suggests that the Kuroshio is the main driver of the mean sea level tilt along the south coast of Japan. Discrepancies in the alongshore tilt of sea level estimated by the geodetic and ocean approaches along both coasts are discussed in terms of errors in the ocean and geoid models.
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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 | 0.000 | 0.000 |
| 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".