Coupled Hydraulic-Thermal Model for Soils under Extreme Weather in Cold Regions
Bibliographic record
Abstract
Extreme weather conditions govern the hydraulic and thermal properties of glacial clay deposits under the cold climate of the Canadian Prairies. The prediction of time-dependent soil behavior over the entire year and under extreme weather conditions is required for the design and construction of buried infrastructure. The main contributions of this research are the development and validation of a coupled soil-atmosphere interaction model to predict transient water and heat movement under mean, extreme dry, and extreme wet weather scenarios. Results indicated that the hydraulic properties are governed by the net water flux that resulted in the shifting of the seasons as follows: mean that comprises winter (3½ months), spring (1 month), summer (5½ months), and fall (2 months); dry that includes spring (4 months), summer (4 months), and fall (4 months); and wet that has winter (4 months), inseparable springsummer (5 months), and fall (3 months). The thermal properties are governed by air temperature for the investigated soil. Identical values of thermal gradient during spring-summer (April to October) in all scenarios indicate that the soil gains more heat compared with the heat loss during fall-winter (November to March), especially for mean and dry conditions. Furthermore, the inflection points in heat flux show that the soil gains heat from May to August and loses heat from September to April. Finally, the active depth of soil was found to be 4 ± 1 m for hydraulic properties and 3 m for heat flux.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".