Reevaluation of Design Waves Off the Western Indian Coast Considering Climate Change
Bibliographic record
Abstract
AbstractKnowledge of design waves with long return periods forms an essential input to many engineering applications, including structural design and analysis. Such extreme or long-term waves are conventionally evaluated using observed or hindcast historical wave data. Globally, waves are expected to undergo future changes in magnitude and behavior as a result of climate change induced by global warming. Considering future climate change, this study attempts to reevaluate significant wave height (Hs) as well as average spectral wave period (Tz) with a return period of 100 years for a series of locations along the western Indian coastline. Historical waves are simulated using a numerical wave model forced by wind data extracted from the archives of the National Center for Environmental Prediction and the National Center for Atmospheric Research, while future wave data are generated by a state-of-the-art Canadian general circulation model. A statistical extreme value analysis of past and projected wave data carried out with the help of the generalized Pareto distribution showed an increase in 100-year Hs and Tz along the Indian coastline, pointing out the necessity to reconsider the safety of offshore structures in the light of global warming.
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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 | 0.001 |
| 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".