Use of an artificial neural network model for estimation of unfrozen water content in frozen soils
Bibliographic record
Abstract
A portion of pore water is typically in a state of unfrozen condition in frozen soils due to the complex soil-water interactions. The variation of the amount of unfrozen water and ice has a significant influence on the physical and mechanical behaviors of the frozen soils. Several empirical, semi-empirical, physical and theoretical models are available in the literature to estimate the unfrozen water content (UWC) in frozen soils. However, these models have limitations due to the complex interactions of various influencing factors that are not well understood or fully established. For this reason, in the present study, an artificial neural network (ANN) modeling framework is proposed and the PyTorch package is used for predicting the UWC in soils. For achieving this objective, extensive UWC data of various types of soils tested under various conditions were collected through an extensive search of the literature. The developed ANN model showed good performance for the test dataset. In addition, the model performance was compared with two traditional statistical models for UWC prediction on four additional types of soils and found to outperform these traditional models. Detailed discussions on the developed ANN model, and its strengths and limitations in comparison to different other models are provided. The study demonstrates that the proposed ANN model is simple yet reliable for estimating the UWC of various soils. In addition, the summarized UWC data and the proposed machine learning modeling framework are valuable for future studies related to frozen soils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".