The effects of water content on mineralogical and drainage quality dynamics in weathering mine waste rock
Bibliographic record
Abstract
• Quantitative reconciliation of mineralogical data with leachate dynamic. • Investigation of mine waste rock water content on weathering. • Decrease in leachate pH at lower water saturation. • Carbonate phase size reduction with increase in water saturation. Water content plays a critical yet ambivalent role in the physical and geochemical stability of mine tailings and waste rock, but its effects on individual chemical weathering reactions (dissolution and precipitation) and bulk water quality remain poorly understood. We experimented with synthetic waste rock to quantitatively reconcile modal and textural mineralogical parameters (mineral size, association, and liberation) with leachate quality at different water saturation levels (5–100%). Leachate pH consistently decreased from a 13-week average of 6.14 at 100% saturation to 5.37 at 5% saturation. Simultaneously, mineralogical analyses revealed a reduction in the size of carbonate phases over the course of weathering, decreasing from 200 µm to 145 µm as water saturation increased from 5% to 100%, which could be a contributing factor to more alkaline pH in samples with higher water saturation. Total concentrations of iron (Fe) and copper (Cu) in solution increased with lower moisture, with Cu concentrations increasing 24-fold in samples at 5% saturation compared to full saturation. In contrast, arsenic (As) concentration increased with higher moisture levels, which we attribute to reduced galvanic protection of As-bearing pyrite from oxidation. Multivariate statistical evaluation allowed us to uncover correlations between water chemistry and mineralogical data (i.e., sulfide associations or phase perimeters) and to explain chemical differences between samples, particularly those at 100% and 5% water saturation. Our findings underscore the importance of conducting quantitative mineralogical investigations on mine waste materials and considering their stored water content in the formulation of mine waste management strategies.
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 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".