Estimation of Local Ice Pressure Using Up-Crossing Rate
Bibliographic record
Abstract
Ice load estimation is required in the design of ships and offshore structures for arctic and subarctic conditions. This paper focuses on the estimation of local ice pressures. The “event-maximum” method for local ice pressure analysis is a probabilistic method 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 new probabilistic 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 for the selected data set; a more comprehensive set of results for the analysis of available ship-ice interaction data is presented in a companion paper. For a sample case, local ice pressure estimates obtained using the up-crossing rate method are compared with results obtained 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. For design curves based on the data set considered, estimates using the event-maximum method were more conservative than those obtained using the up-crossing rate method. The up-crossing rate approach is promising; analysis of additional data sets is recommended to allow broader comparison of the methods.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".