Reinforcement of ice covers: a summary of previous full-scale scenarios and relevance to Canada’s winter road infrastructure
Bibliographic record
Abstract
Winter roads are seasonal routes that only exist in the winter – they run over land and over frozen water surfaces (lakes, rivers). The over-ice segments are particularly vulnerable to a warming climate. Ice reinforcement for the purpose of sustaining higher loads, and/or for a longer yearly operational lifespan, may be seen as an effective means to remediate weak links along a winter road, but that technique is not well known. A review of documented cases is presented in which reinforced ice was used in full-scale scenarios, with information on construction and deployment procedures. Retrieval of reinforcement, after the winter road season is over, is another aspect that warrants attention in a planning scheme. A distinction is made between the concepts of ice ‘failure’, linked with ‘first crack’, and ‘breakthrough’, which is a complex phenomenon involving a sequence of radial and circumferential cracks, when a vehicle breaks partly or completely through the ice. Determining the bearing capacity of reinforced ice is seen as an outstanding challenge in being able to implement a safe and effective reinforcement procedure. The solution would be to perform real-world, fully instrumented ice testing, and most importantly, allow for breakthrough to be achieved, so as to capture the full response and assess the ultimate resistance of the ice cover.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".