The effects of culverts on embankment performance in cold regions
Bibliographic record
Abstract
In Arctic regions where permafrost is present, culverts are installed during winter construction where the fill material is placed and compacted under frozen conditions. Culverts affect the thermal and mechanical stability of road embankments. They act as heat conduits during the summer months but depending on its diameter can trap heat during the winter months when their ends are covered with snow. Thawing of the frozen soil around and below the culvert will lead to differential settlements and longitudinal cracks at the road surface and embankment slopes, respectively. In order to study the influence of culverts covered with snow during the winter months on the thermal and mechanical behaviour of frozen fill embankments, a 5-noded thermistor string was installed inside an 800 mm diameter culvert along its length on an existing test section along the Inuvik-Tuktoyaktuk Highway (ITH) in the Northwest Territories, Canada. Camera traps were installed around the research site to monitor snow depth. In addition, heat transfer and coupled thermal-mechanical finite element models were developed using the commercially-available finite element software ABAQUS. The temperatures recorded from this thermistor string were used as a boundary condition. The models were run for two years of recorded field data obtained from the field to provide an understanding in the thermal regime around the culvert and its associated deformations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".