A Thermal Imaging Methodology To Study Evaporation Kinetics in Mine Tailings
Bibliographic record
Abstract
Predicting why, how, and when mine tailings disposal sites become prone to dust scattering events is often hampered by our limited understanding of the factors that affect the drying rates from their surface layers. As a case study, thermal imaging is demonstrated here to be a valuable tool to study the evaporation mechanisms and rates from bauxite residues as a function of their thickness and physicochemical properties, as well as environmental conditions. These investigations reveal their that late stage drying rates are limited by gas phase diffusion through the interstitial air within their internal microporosity. The smallness of the effective diffusion coefficient indicates that water adsorption on bauxite residues surfaces is the dominant phenomenon responsible for their slow water vapour transport kinetics, a phenomenon that ultimately controls their late stage drying rates, that is when dust scattering is most likely to occur. As such, application of this thermal imaging methodology in the field may also contribute to improve the accuracy of risk assessment protocols, support intervention and mitigation strategies, underpin optimization efforts for mining residues management, and improve forecasting of fugitive dust emissions from mine tailings by enabling more accurate predictions of the evolution in their surface drying state.
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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 | 0.000 |
| 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) | 0.001 | 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".