Measuring and Predicting Soil Temperature and Moisture Content Beneath Highways in Nova Scotia, Canada: One Year of Observations and Forecasts
Bibliographic record
Abstract
Heavy traffic during the spring thaw causes faster degradation of the pavement and leads to increased maintenance fees. To reduce the damage, most provinces in Canada implement spring load restrictions for heavy traffic. Given that conditions often veer from climatology, imposing load restrictions at fixed dates every year is not optimal. It can lead to increased road damage or increased costs to the transportation industry. With funding from the Nova Scotia Department of Transportation and Infrastructure Renewal (NSTIR), probes capable of measuring both soil temperature and moisture to a depth of 110 cm were installed at six different locations around Nova Scotia in fall 2010. Immediate uses of the observations by NSTIR are discussed. An objective guidance tool was developed to help departments of transportation to determine the beginning of the seasonal load adjustment periods, far enough in advance for the road transportation industry to adjust their plans. To achieve this, a soil energy and mass balance model was used to predict soil temperature. The model, SNTHERM, is initialized with the probe observations and driven by atmospheric conditions from numerical weather prediction models. Simulations are run daily, out to 5 days for the six Nova Scotia sites and for one site in the Northwest Territories. Results to date are promising and indicate that it is possible to provide advance warning of the spring thaw in roads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".