Bibliographic record
Abstract
Extreme wind and snow loads can be critical structural design parameters for Alaska. Understanding these extreme climate conditions under climate change conditions is valuable for adaptation and community resilience. Wind or snow loads can be the principal climatic loads, and the other can be the companion load. The effect of climate change on these two extreme climatic variables deserves investigation. Moreover, how climate change impacts both extreme climatic variables is still being determined; it is also worth understanding the spatial effect of climate change over Alaska for each hazard. For this purpose, this study investigated a large ensemble of simulations under a high-emissions climate change scenario to analyze the uncertainties of the future extreme wind climate and snow loads for Alaska. The impact of climate change on the design level of extreme events was investigated. The nonstationarity of the extreme wind speeds and ground snow loads under climate change scenarios were examined. The effects of the nonstationarity on determining the design wind speeds and ground snow loads are discussed. The correlation between the extreme wind speeds and ground snow loads is explored to understand how to consider the companion climatic loads in climate change scenarios. Uncertainties associated with future projections are explored. Structural design for climate change adaptation and community resilience in extreme wind and snow hazards is discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".