On the Potential of Nuclear Magnetic Resonance for Assessing Water Content and Saturation in Mine Tailings
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".