Chemical-Physical Characterization of Stava Tailings Subjected to an Innovative Aging Technique
Bibliographic record
Abstract
Tailing dams are realized to store the waste products resulting from the mining extraction processes.These complex geotechnical structures should be designed taking into account long-term stability and long-term properties of the deposited materials.Depending on the interactions between source mineralogy and local conditions, tailing wastes can undergo aging processes with chemical and physical modifications.Recently, in many countries tailing wastes are re-used as feedstock for cement and concrete, backfill or landscaping material, so if any, the long-term chemical and physical modifications could affect the hydro-mechanic response of tailings, resulting in relevant environmental and economic consequences.An increased interlocking of particles and oxidation, sometimes making previously safely held contaminants available and mobile, are recognized as common aging processes.Among the long-term aging processes, the natural ionizing radiation due to ultraviolet rays or cosmic rays can be considered.Moving from these reasons, this paper presents an innovative accelerated aging technique to simulate the natural ionizing radiation from the sun.Tailing fluorite ore samples collected form the collapsed Stava dams (Italy) were characterized in dry and wet conditions, before and after the gamma rays treatment.Stava silty tailings showed some physical modifications in terms of specific surface, size particle distribution and inner porosity of the particles, while they revealed a certain chemical stability.
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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 | 0.001 | 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) | 0.001 | 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".