Simulation and analysis of ice-induced vibrations experienced but Molikpaq during the May 12, 1986 event
Bibliographic record
Abstract
Much attention has been given to the dynamic ice-structure interaction of the Molikpaq caisson which resulted in severe, almost catastrophic, structural vibrations during the winter of 1985- 1986 at Amauligak I-65 in the Canadian Beaufort Sea. In this study, specific focus is given to the scientific literature describing the ice-induced vibration event on May 12, 1986 over the observed range of ice conditions and drift speeds. While considering the limitations of the measurement data available for the event, the scenario is reviewed and a recent phenomenological model is implemented to simulate the ice-induced vibrations observed. A simplified model of the Molikpaq caisson is simulated to interact with an ice floe and the results are compared with the full-scale observations from the event. Limitations of the modeling with respect to the available full-scale data are discussed and modeling results are compared to previous simulations attempting to explain the event on May 12, 1986. It is concluded that this ice-induced vibration event should be treated with caution and detailed considerations of the scenario, including a comprehensive structural model, must be implemented for accurate simulation of the event on May 12, 1986. Models and theories derived exclusively from this event should be scrutinized in light of its uncertain and complex conditions and thus treated skeptically.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".