Seasonal pattern of matric suctions in highway subgrades
Bibliographic record
Abstract
The performance of “thin membrane surface” (TMS) highways is largely controlled by the strength of the subgrade soil, which in turn is a function of matric suction. Thermal conductivity matric suction sensors were used to indirectly measure in situ matric suctions. In September 2000, 32 thermal conductivity sensors were installed under TMS highways at two locations; namely, Bethune and Torquay, Saskatchewan, Canada, to monitor matric suction and temperature changes with time. This paper presents and interprets the variations in matric suctions measured between the years 2000 and 2005 at the Torquay site. The readings in the field showed a clearly seasonal pattern of matric suction changes that bore a relationship to rainfall near the test sites. Relatively constant equilibrium matric suctions that ranged from 20 to 60 kPa throughout the years were measured under the driving lanes. Conversely, matric suctions under the side slopes were found to vary widely with time and depth, ranging from 100 to 1500 kPa over the years. The greatest variation in matric suction from one location to another location occurred during the month of April. The matric suctions showed low variability during June, whereas larger variations were observed from July to October.
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 | 0.001 | 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) | 0.001 | 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".