Valorization of residual materials in the prevention and treatment of contaminated mine water and air
Bibliographic record
Abstract
Valorization of residual materials, close to mine sites, reduces the environmental footprint and helps balance the ratio of rentability to responsible management of natural resources. Relevant case studies using raw vs modified, organic vs inorganic residual materials for contaminated mine water prevention and efficient treatment, using active and passive treatment processes are presented. Main findings show: 1) monoand multi-layer solid covers installed on top of fresh or weathered mine tailings have beneficial role in limiting oxygen and water ingress to reactive minerals and mine drainage generation; 2) reactive mixtures composed of natural and residual materials are efficient in (semi)-passive treatment of contaminated mine drainage from covered tailings; 3) contaminated residues from active and passive treatment of mine water can efficiently be used in the revegetation of rehabilitated mine sites; 4) modified materials (efficiency enhancement) with porous structure and basic character (functional groups connected to their surface) are effective for the capture of acid gases such as SO2 emitted from mining and metallurgical industries, and water contaminants from forestry lumber and mining. Valorization of raw and modified materials for the prevention and treatment of contaminated mine water and air is promising for circular economy development in remote regions as well as the reduction of the environmental footprint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".