Ice Ridge Keel Properties and Loading at the Confederation Bridge for 2008
Bibliographic record
Abstract
Ice ridge loads observed over the last twelve years on the Confederation Bridge in Eastern Canada have been smaller than predicted during design. Previous studies (Lemee and Brown, 2003; ElSeify and Brown, 2006) suggested that the ice loading due to ice ridge/structure interactions at the Bridge are controlled by the consolidated layer of the ice ridge and that keel ice rubble contributes minimally to the load, because the keel rubble is broken up by the leading edge of the ice shield. In this study, the keel cross-sections in the Northumberland Strait were catalogued during February of 2008 ice season using sonar data and video records. A total of 89 keel cross-sections with depths exceeding three meters were included. The width, depth, side angles, and areas were recorded for each cross-section. These measurements were combined with weather, video, and load data to determine how, if at all, keel morphology was affecting ice loading on the Bridge. The keel area was examined to determine if it was a more appropriate keel geometric property than keel depth to use in keel load prediction. Keel morphology have weak relationships with the maximum loads. The maximum loads generally increased with the maximum keel depth until the keel depth reached approximately 4 meters, the depth of the ice shield below water surface, at which stage the relationship weakened.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".