Thermal performance of sloped thermosyphons installed at the Dry Creek highway section, Yukon, Canada
Bibliographic record
Abstract
A total of 58 sloped thermosyphons were installed to arrest permafrost thaw beneath a section of the Alaska Highway at Dry Creek, Yukon, Canada.The highway foundation consists of warm (> -0.5 °C) permafrost with massive ground ice that is locally in excess of 9 m thick.In the fall of 2019, construction commenced with minimal vegetation clearing within the right-of-way and shallow excavation along the east tow of the highway embankment.Thermosyphons were installed in cased boreholes drilled beneath the embankment at an 11° incline, approximately every 7 m on centre.Each thermosyphon unit consisted of a single 19.5 m 2 radiator attached to an approximately 35 m long, 76 mm diameter schedule 80 evaporator pipe.The thermosyphons have increased winter heat loss from the foundation since completion of installation in 2020.Permafrost temperature has decreased by several degrees and the permafrost table has vertically aggraded up to 2.5 m above its pre-construction position.The greatest ground cooling has occurred beneath the thickest section of embankment fill which acts to reduce heat gain during the thawing season.The thermosyphons are expected to continue to contribute to permafrost stabilization over the 30-year design life.This project contributes to evaluation of techniques for the adaptation of highway infrastructure to climate change in permafrost environments.1
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".