Bibliographic record
Abstract
The performance of rural highways in Saskatchewan, Canada, constructed of a thin pavement structure is largely controlled by the strength of subgrade soil. The subgrade of these highways consists of compacted unsaturated soil and its strength is a function of net normal stress and soil suction. In situ soil suctions can be measured using indirect technologies such as thermal conductivity suction, (TCS), sensors. Thirty-two thermal conductivity sensors were installed under Thin Membrane Surfaces, (TMS), at two highway locations in southern Saskatchewan, Canada. Soil suctions have been monitored at these sites for more than 10 years. The soil suction readings in the field showed a response to rainfall conditions at the test sites. Changes in soil suction on the shoulder of the road appeared to be mainly due to run-off and infiltration. Relatively constant equilibrium suctions were encountered below the pavement. Suction changes throughout the year were similar from one year to the next. The thermal conductivity TCS sensors performed well under harsh weather conditions including freeze-thaw conditions. An understanding of the soil suction and temperature change behavior of the subgrade throughout the year was obtained from the data.
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".