Ex situ hyperspectral sensing and machine learning for tailings characterization
Bibliographic record
Abstract
Understanding tailings properties at high spatial resolutions is needed for many geotechnical analyses. While tailings properties can be characterized using undisturbed samples and in situ tests, the current methods face limitations regarding adequate spatial resolution and comprehensive property assessment. This study explores the use of hyperspectral sensing and convolutional neural networks for the simultaneous prediction of 12 tailings properties, including percent sand, silt, clay, fines content, solids content, gravimetric moisture content, volumetric moisture content, saturation, void ratio, porosity, total density, and dry density. Tailings from a precious metal mine were used to prepare samples with diverse properties and hyperspectral data were captured. The tailings-hyperspectral dataset was then split into training and testing subsets, a convolutional neural network was optimized and trained, and the model’s performance was assessed using the testing data. The prediction of particle size distribution metrics and moisture metrics have root mean squared errors below 8% with coefficients of determinations above 0.95. Predictions of void ratio, porosity, total density, and dry density have poorer performance than other properties. However, density predictions have lower errors for samples with high saturation. Results show promise for the rapid characterization of tailings properties using hyperspectral data.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".