Database and time series of nearshore waves along the Alaskan coast from the United States-Canada border to the Bering Sea
Bibliographic record
Abstract
First posted March 13, 2024 For additional information, contact: Pacific Coastal and Marine Science CenterU.S. Geological Survey2885 Mission St.Santa Cruz, CA 95060 Alaska's Arctic coast has some of the highest coastal erosion rates in the world, primarily driven by permafrost thaw and increasing wave energy. In the Arctic, a warming climate is driving sea ice cover to decrease in space and time. A lack of long-term observational wave data along Alaska's coast challenges the ability of engineers, scientists, and planners to study and address threats and effects from wave-driven erosion and flooding. To overcome the lack of available observational wave data in the nearshore in this study by the U.S. Geological Survey, waves were downscaled with the Simulating WAves Nearshore numerical wave model (SWAN) for the hindcast period of 1979 to 2019 from the United States-Canada border to the Bering Sea utilizing nine model domains. For each domain, the model was forced at the open boundary with 2,500 representative "sea states," which are likely combinations of significant wave heights, mean wave periods, mean wave directions, and wind speeds and directions. The sea states were obtained from the European Centre for Medium-Range Weather Forecasts "ERA5" dataset for reanalysis of winds and waves using a multivariant maximum-dissimilarity algorithm. The SWAN runs created a downscaled wave database at each grid point, which was used to reconstruct the 40-year time series in the nearshore along the 5- and 10-meter isobaths at locations approximately 400 m apart and corresponding to transects spaced approximately 50 m alongshore, as developed for USGS shoreline-change assessments. Reconstructed time series were compared to observations to validate the numerical model and the downscaled wave database method and showed overall good agreements.
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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 | 0.003 |
| 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) | 0.000 | 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 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".