Evaluating the use of machine learning for moisture content prediction in base and subgrade layers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Subgrade moisture content significantly influences soil strength and pavement bearing capacity. Pavement moisture content varies greatly throughout the year, especially in cold regions. Thus, having a better understanding of seasonal variation in moisture content in the pavement is needed to be developed. This research aims to apply machine learning models to predict the moisture content of unbound materials in the pavement. Unfrozen volumetric moisture content measurements recorded at the Integrated Road Research Facility test road in Edmonton, Alberta were used to train machine learning models to predict moisture content at depths within 2.7 m of the road surface. Machine learning models were implemented based on three parameters of pavement temperature, day of the year and depth. The results from the machine learning model were compared with a statistical model and showed higher accuracy than the existing model, indicating that machine learning models could enhance moisture content prediction in the pavement.
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.
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 it