Increases in highway maintenance costs in a permafrost environment undergoing climate change, Yukon, Canada
Bibliographic record
Abstract
Assessment of infrastructure vulnerability to climate change in permafrost environments has emphasized increases in ground temperature, deepening of the active layer, and differential settlement.Hydrological factors are less emphasized.Yukon's Transportation Maintenance Database records expenses for 61 activities on the 21 maintenance sections of the territorial highway network.The costs associated with snow clearing, icing control, washout repair, and landslide removal have been examined from April 1994 to March 2022.These are directly related to climate and are primarily associated with hydrologic processes.For Yukon's entire highway network, climate-related maintenance expenditures have increased by $169,000 per year (in constant 2021 CA$) since 1994.Topography, surficial deposits, permafrost, and climate create specific sub-regional financial responses to a changing environment.For example, snow clearing expenditures are greatest for highways in the Coast Mountains region (now over $600,000 per year), icing control expenditures dominate on the Silver Trail in the discontinuous permafrost zone (over $250,000 annually), while intermittent clearing of landslides and repair of washouts are greatest for the Ogilvie section of the Dempster Highway in steeply sloping terrain and continuous permafrost ($825,000 in 2013).This paper presents expenditure profiles for seven representative maintenance camps in distinct physiographic and permafrost environments within the highway network.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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".