Measuring the In Situ Density and Moisture Content of Filtered Tailings Using an Electrical Density Gauge
Bibliographic record
Abstract
Due to the logistical challenges associated with using nuclear densitometers at remote sites, the industry is seeking an alternative method to determine the in situ density and moisture content during the construction of filtered tailings facilities. This study aims to investigate the impact of salinity on soil electrical properties and evaluate the feasibility of using an electrical density gauge (EDG) to measure the in situ density and moisture content of saline filtered tailings. The results indicate a dependence of electrical measurements on salinity. To develop procedures for soil calibration models of filtered tailings, standard Proctor tests were first conducted using Devon silt. These procedures were then applied to the filtered tailings to establish correlations between electrical properties (dielectric constant, impedance, capacitance-to-resistance ratio) and physical properties (density and moisture content) at varying salinities. It is suggested to build the soil calibration model using an EDG within a water content range of 10% to 18%. Furthermore, the effectiveness of the developed calibration models has been validated, demonstrating the applicability of the EDG instrument for filtered tailings in a saline environment. However, applying the salinity correction is crucial when the sample has a considerably different salinity than the calibration model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".