Soil Wettability As Determined from Using Low-Field Nuclear Magnetic Resonance
Bibliographic record
Abstract
The molarity of ethanol droplet and water drop penetration time methods are commonly used to determine soil wettability because these tests are quick and easy to perform. However, these tests do not provide reproducible results on the same sample. Low-field nuclear magnetic resonance (NMR) is shown as an alternative tool to determine soil wettability. Addition of small amounts of water in dry wettable porous media produces predominant amplitude peaks at transverse relaxation times (T2) of 100 ms or less while addition of water in dry water-repellent porous media with the same pore structure produce predominant amplitude peaks at T2 values near 1000 ms. The geometric mean of T2 (T(2gm)) from water-repellent samples immediately after the addition of water is greater than 1000 ms, which is close to that of bulk water, while T(2gm) from wettable samples immediately after the addition of water is significantly less than 1000 ms. Measurements over time show that water-repellent samples eventually reach the same equilibrium end point as its corresponding wettable sample when continually exposed to water. This paper will show that NMR can be used to formulate a screening criterion for quickly determining wettability. The advantage of using NMR is that the results are reproducible provided the sample is prepared and analyzed in a systematic manner.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".