Extreme Value Estimation of Beaufort Sea Ice Dynamics Driven by Global Wind Effects
Bibliographic record
Abstract
Abstract The purpose of the present study is to investigate the extreme values of the ice drift speed, which are also considered in the light of the magnitude of the simultaneous wind speed. The relationship between wind speed and ice drift speed is studied. The long-term ice drift data is collected by using local subsurface measurements based on acoustic Doppler current profilers (ADCP) in the Beaufort Sea during the period of 2006–2017. Upward-looking sonars (ULS) are deployed in order to observe the ice thickness as well as to identify events that correspond to open water conditions. The relationship between the ice drift speed and the wind speed is also investigated. It is found that the magnitude of the average ice drift speed is approximately 2.5% of the wind speed during the winter season. Estimation of the extreme values of the ice drift speed is studied by application of the average conditional exceedance rate (ACER) method. It is found that the extreme ice drift speed during the ice melt season (i.e. the summer season) is approximately 20%–30% higher than that during the ice growth season (i.e. the winter season). The extreme ice drift speed can be effectively estimated based on the 2.5% wind speed. Moreover, the extreme ice drift speed can be obtained based on the extreme values of 2.5% of the wind speed based on multiplying with an amplification factor which varies in the range from 1.7 to 2.0 during the growth season, corresponding to increasing return periods of 10, 25, 50 and 100 years.
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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) | 0.000 | 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 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".