On the Potential of Nuclear Magnetic Resonance for Assessing Water Content and Saturation in Mine Tailings
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
Nuclear magnetic resonance (NMR) exploits the interaction between atomic nuclei and an external magnetic field. Recent advancements in small-diameter probes have expanded NMR applications for shallow subsurface investigations (<100 m); however, existing efforts in tailings engineering remain scarce. This study evaluates NMR’s potential to characterize water content and saturation in mine tailings. Tailings with varying particle size distributions and mineralogies, along with Ottawa sand and kaolinite, were analyzed using two NMR systems with different signal-to-noise ratios and magnetic fields. The study examines the influence of magnetic susceptibility, mineralogy, gradation, echo time, signal-to-noise ratio, and tailings pond water on NMR measurements. NMR-derived water content and saturation estimates were compared against controlled target volumetric and gravimetric measurements. Results indicate that magnetic susceptibility is a key limiting factor: NMR performed well for paramagnetic tailings with low magnetic susceptibility (<1.0 E-3) but poorly for ferromagnetic tailings with high magnetic susceptibility (>1.9E-2). However, low magnetic susceptibility alone does not guarantee reliable performance, as mineralogy and the presence of elements such as iron (Fe) also play a role. Additionally, the results show that shorter echo times and higher signal-to-noise ratios are beneficial. While gradation and tailings pond water primarily influenced NMR decay curves, they had minimal impact on water content estimates for the examined paramagnetic tailings. Finally, the study conducts error propagation evaluations to assess the degree of confidence in estimating volumetric water content and degree of saturation for different scenarios in tailings engineering.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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