Using the Event Maximum Method to Further Analyze Full Scale Local Pressure Data
Bibliographic record
Abstract
Abstract Extreme values for local ice pressure are a primary consideration in the design of local structure for ships and offshore structures in arctic environments. ISO 19906 (2010) includes guidelines for a probabilistic approach in determining the local design pressure for Arctic offshore structures. However, the standard is vague in how it should be used. The probabilistic method employed in the standard is the event maximum method developed by Jordaan et al. (1993). It accounts for the expected exposure of the local structure to ice pressure and includes a constant, a, used to describe the relationship between local pressure and area. The constant a is derived in Jordaan et al. (1993) and extended in Jordaan et al. (1997) and reported in Taylor et al. (2010). The a-area relationship is based on the local pressure values from the very aggressive multi-year ridge rams of the CANMAR Kigoriak trial (1982). This leads to a very conservative a-area relationship and may be excessive for some ice conditions. This paper includes an explaination of the use of the event maximum method in ISO 19906. Local pressure data obtained through shear strain gauge systems from Polar Sea (1983) and Oden (1991) have been reanalyzed using the event maximum method (Jordaan et al. 1993, 1997). The data has been sorted by both ice thickness and ice concentration to investigate the existence of a trend between ice thickness and local pressure.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".