Machine Learning–Enhanced Modeling of Stress–Strain Behavior of Frozen Sandy Soil
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
Many experiments and computational techniques have been employed to explain the mechanical properties of frozen soils. Nevertheless, due to the substantial complexity of their responses, modeling the stress–strain characteristics of frozen soils remains challenging. In this study, artificial neural networks (ANNs) were employed for modeling the mechanical behavior of frozen soil, while different testing strategies were carried out. A database covering stress–strain data from frozen sandy soil subjected to varying temperatures and confining pressures, resulting from triaxial tests, was compiled and employed to train the model. Subsequently, different artificial neural networks were trained and developed to estimate the deviatoric stress and volumetric strain, while temperature, axial strain, and confining pressure were considered as the main input variables. Based on the findings, it can be indicated that the models effectively predict the stress–strain behavior of frozen soil with a significant level of accuracy.
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 | 0.000 | 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) | 0.003 | 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