Modeling sea‐level processes on the U.S. Atlantic Coast
Bibliographic record
Abstract
Abstract One of the major concerns engendered by a warming climate are changing sea levels and their lasting effects on coastal populations, infrastructures, and natural habitats. Sea levels are now monitored by satellites, but long‐term records are only available at discrete locations along the coasts. Sea levels and sea‐level processes must be better understood at the local level to best inform real‐world adaptation decisions. We propose a statistical model that facilitates the characterization of known sea‐level processes, which jointly govern the observed sea level along the United States Atlantic Coast. Our model not only incorporates long‐term sea level rise and seasonal cycles but also accurately accounts for residual spatiotemporal processes. By combining a spatially varying coefficient modeling approach with spatiotemporal factor analysis methods in a Bayesian framework, the method represents the contribution of each of these processes and accounts for corresponding dependencies and uncertainties in a coherent way. Additionally, the model provides a consistent way to estimate these processes and sea level values at unmonitored locations along the coast. We show the outcome of the proposed model using thirty years of sea level data from 38 stations along the Atlantic (east) Coast of the United States. Among other results, our method estimates the rate of sea level rise to range from roughly 1 mm/year in the northern and southern regions of the Atlantic coast to 5.4 mm/year in the middle region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
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 teacher head, 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".