Re-analysis of load and pressure data acquired from ice impacts during the CCGS Louis St-Laurent 1994 Arctic voyage
Bibliographic record
Abstract
Ice load and pressure data from the 1994 CCGS Louis St-Laurent trip through the Arctic were reanalyzed using a different method than was used for the original report. The impact events that were chosen for re-analysis spanned a variety of ship speeds, ice thicknesses and concentrations. For any given impact event all sub-panel segments (cells) of the strain-gauged hull panel for which a signal registered above the deemed noise level were considered to be subjected to ice load. Consequently all of the ice contact area, and associated ice load, was taken into account regardless of whether the ice contact consisted of a single area or multiple non-contiguous areas on the impacted panel. The data from a total of 51 randomly selected events covering a range of ice contact areas from 1.4 m2 to 16.8 m2 were initially analyzed to yield load and pressure distribution at the time of peak load for each event. Then further analysis was conducted to identify well-behaved events where the loaded areas were more contained on the panel and less concentrated at its edges. The effects of using this selection strategy are shown. For the data from the well-behaved events the pressure-area relationship was essentially flat with an average value 0.49 MPa over the stated range of contact area. Another set of 12 impact events were also analyzed, for which the full time-series of pressure on each of the 30 cells that comprise the panel were available from the original reduction of data. An analysis of load and contact area from these events demonstrated the effect of having oversized cells on ice impact panels, that leads to overestimates of ice contact area and underestimates of average pressure. The analysis suggested that the best estimates of actual ice contact area and average pressure, though still not fully accurate, are obtained when the contact area is the greatest, i.e. usually at peak load.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".