Estimating soil-water characteristic curve from soil-freezing characteristic curve for mine waste tailings using time domain reflectometry
Bibliographic record
Abstract
The unsaturated properties of a soil are required to predict the rate of dewatering and magnitude of strength gain of a mine waste tailings deposit during desiccation dewatering. This prediction requires the soil-water characteristic curve (SWCC), which is time-consuming and challenging to attain and may take anywhere from weeks to months to complete a single test. As a result, alternative methods are needed to estimate the SWCC. Past research has indicated that the soil-freezing characteristic curve (SFCC) can be used to estimate the SWCC in some soils. An experimental method and apparatus were developed to measure the SFCC to estimate the SWCC for different mine waste tailings, including copper tailings, gold tailings, and oil sands centrifuge cake. The experimental method involved using a resistance temperature detector to measure the temperature and time domain reflectometry to determine the unfrozen water content of the soil. The results showed that the SFCC could be used to estimate the SWCC for tailings from metal mines (gold tailings and copper tailings) with a high portion of sand-sized particles and a small amount of clay-sized particles, but was not able to estimate the SWCC for oil sands tailings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".