Predicting the Freezing Characteristics of Organic Soils Using Laboratory Experiments and Machine Learning Models
Bibliographic record
Abstract
Frozen ground regions have recently experienced increasing construction activity due to the vast undeveloped resources they contain. However, frozen soils exhibit thermal and mechanical properties that differ substantially from those of temperate soils, leading to a range of engineering challenges. This study investigates the influence of organic matter content on the freezing behavior of soils through a series of laboratory experiments and machine learning (ML) modeling. Soil samples were collected from Alberta, Canada, and Gangwon Province, South Korea, and their organic matter contents were adjusted using the loss-on-ignition method combined with peat moss addition. Standard Proctor compaction tests and uniaxial compression tests under subzero conditions were performed to evaluate compaction characteristics and strength development. The unfrozen water content was measured at different subzero temperatures to assess thermal and hydraulic responses. The resulting experimental dataset was then used to develop ensemble ML models—random forest (RF) and extreme gradient boosting (XGB)—for predicting unfrozen water content. The results indicate that higher organic matter contents reduce compaction efficiency, increase residual unfrozen water content, and influence strength development under freezing conditions. Both RF and XGB achieved high predictive accuracy, demonstrating their potential as reliable tools for evaluating the freezing behavior of organic soils.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".