Estimation of Local Ice Pressure Using Up-Crossing Rate
Bibliographic record
Abstract
Ice load estimation is required for offshore structures designed for arctic and sub-arctic conditions. This paper focuses on the estimation of local ice pressures. The ‘event-maximum’ method for local ice pressure analysis is based on the maximum pressure of a given event; other local peaks in the data are not included. To study how this may affect local ice pressure estimates, a method based on the up-crossing rate was developed. Field data from 1982 Polar Sea arctic trials in the Beaufort Sea are processed as a time series. Up-crossing rates at different local pressure levels are obtained for local areas of interest. A relationship between up-crossing rate and local pressure-area results is established. Results from the analysis of full-scale data using the event-maximum method are presented in a companion paper. For a sample case, local ice pressure estimates obtained using the up-crossing rate method are compared with those presented in the companion paper, based on analysis using the event-maximum method. The local pressure-area relationship is found to be similar for both the up-crossing rate method and the event-maximum method. Both methods and corresponding analysis results are compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".