A Simple Method for Estimation of the Soil Pore Structure in Frozen Soils Using the Nuclear Magnetic Resonance Method
Bibliographic record
Abstract
The nuclear magnetic resonance method (NMR) is widely used as a tool in the pore size characterization of rocks such as sandstones, carbonates, and coals. This technique also has been widely used to estimate the unfrozen water content in frozen soils; however, there are limited studies that focus on the frozen soils pore structure characterization. In the NMR tests, based on the results from the literature, a linear relationship is typically assumed between the pore size (R) and transverse relaxation time (T2), of the transverse magnetization decay. This relationship is mathematically represented as 1T2=2ρ2R, where ρ2 is a constant representing the surface relaxivity. Traditionally, ρ2 is determined by T2 cutoff values, which is a relaxation time threshold that divides the T2 spectrum into two zones: namely, the bound water and free water in frozen soils. Conventionally, centrifuge experiments are used for determining the cutoff value in the field of petroleum engineering. The key parameter ρ2 is also approximately estimated or assumed based on the information from the published literature for different types of soils. In other words, T2 cutoff value is based on approximations that have limitations or based on centrifuge tests that need elaborate testing which is expensive. In this paper, a new method is proposed to calculate T2 cutoff value from the NMR test results on a saturated frozen silt clay. The key advantage of this method is that it alleviates the approximations or the use of expensive centrifuge tests for the estimation of the cutoff value, T2. The proposed method in this paper is useful for better understanding the behavior of frozen soils with the aid of simple and inexpensive methods.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".