Evolving extreme events caused by climate change: A tail based Bayesian approach for extreme event risk analysis
Bibliographic record
Abstract
Natural hazards are of significant concern for engineering development in the offshore environment. Climate change phenomena are making these concerns even greater. The frequency and extent of natural hazards are undesirably evolving over time; so risk estimation for such events require special consideration. In most cases the existing extreme models (based on the extreme value theory) are unable to capture the changing frequency and extremeness of natural hazards. To capture the evolving frequency and extremeness of natural hazards and their effects on offshore process operations, an advanced probabilistic approach is proposed in this paper. The approach considers a heavy right tail probability model. The model parameter is estimated through the Bayesian inference. Hill and the SmooHill estimators are used to evaluate the lowest and highest exponent of the probability model. The application of the approach is demonstrated through extreme iceberg risk analysis for the Jeanne d’Arc basin. This study shows climate change or global warming is causing to appear a significant number of icebergs every year in the study area. Offshore structures are often designed to withstand the impact of 1 MT icebergs weight; however, the study observes large icebergs (10 MT weight) are sighted in recent years (14% of the total number of cited icebergs for the period of 2002–2017). As a result, the design philosophy needs to be revised. The proposed risk-based approach provides a robust design criterion for offshore structures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".