Ground-Penetrating Radar Evaluation of Moisture and Frost across Typical Saskatchewan Road Soils
Bibliographic record
Abstract
This study was undertaken to evaluate the effect of soil type, moisture content, and the presence of frost on road substructure permittivity. Permittivity sensitivity of typical road soils was characterized in the laboratory to provide baseline dielectric constant values which were compared to field ground penetrating radar (GPR) survey results. Both laboratory devices, the complex dielectric network analyzer and the Adek Percometer, as well as the field GPR system were used in this study to measure the dielectric constant of soils. All three systems differentiated between coarse-grained and fine grained soils. In addition, at temperatures below freezing, all three systems identified an increase in water content in soils; however, when frozen, the sensitivity of dielectric constant across soil type and moisture content was significantly reduced. Based on the findings of this study, GPR technology has the ability to characterize in situ substructure soil type and moisture content of typical Saskatchewan road substructure soils. Given the influence of road soil type and moisture content on in-service road performance, this ability could provide road engineers with accurate estimates of in situ structural condition of road structures for preservation and rehabilitation planning and optimization purposes.
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 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 | 0.000 | 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 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".