Beam testing of reinforced ice in the context of winter roads
Bibliographic record
Abstract
A laboratory study was conducted to provide additional information on ice reinforcement and its field implementation to strengthen segments of floating ice (referred to as ice bridges, ice crossings or ice roads) that are commonly weak links in a winter road operation. In addition to preventing breakthroughs, this option would increase the predictability of the ice’s bearing capacity. Four-point beam bending tests were conducted on freshwater ice with and without reinforcement, for comparison purposes. A steel mesh, a polypropylene geogrid and threaded steel rods were used as reinforcement material. In all tests, the load and the loading rate increased with time up to a peak load. For the non-reinforced ice, there was a sudden drop in load at which point the tests ended. For the reinforced ice, a drop in load also followed the peak load, but the load climbed again up to another peak in a repetitive fashion. The ice reinforced with the threaded rods had the highest resistance (exceeding the load cell capacity). Thin section observations showed that the crystal structure was forming around the material. However, cleavage surfaces along the ice/material interface after beam failure indicate that the interface could be a strength reduction factor.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".