Compositional heterogeneity of secondary minerals in mine waste rock: Origins and implications for water quality
Bibliographic record
Abstract
Secondary minerals in mine waste materials impose strong controls on water quality by scavenging solutes of concern. This study investigates the mineralogical and compositional characteristics of secondary Fe(oxy)hydroxides and Ca-sulfates, two globally ubiquitous secondary precipitates, in weathered mine waste rock. Bulk analyses show that Si, Ca, Fe, Al, and S-bearing primary phases were the most abundant in the entire samples, but up to a few wt% of secondary Fe(oxy)hydroxides and Ca-sulfates were present as well. In these secondary phases, trace metal impurities like V (37 mg/kg in Ca-sulfates), Cr (23 mg/kg in Fe-oxides and 21 mg/kg in Ca-sulfates), and Cd (up to 15 mg/kg in Fe-oxides and Ca-sulfates) could not be detected by bulk techniques (XRF and XRD), but their deportment to some extent characterized by automated mineralogy, and their spatial distribution assessed at high-resolution through microscale LA-ICP-MS analysis. Element mapping revealed that metal(loid)s were generally enriched at grain rims, reflective of peripheral sequestration through surface adsorption. An exception was V, which was uniformly distributed in the studied secondary Fe-oxides, suggestive of isomorphic substitution during co-precipitation. Factor analysis revealed distinct groups of elements co-associated within each secondary mineral (e.g., divalent transition metal cations versus oxyanionic metalloids), likely caused by similar primary mineral sourcing or a comparable sequestration mechanism. Our results demonstrate the importance of secondary mineral precipitates for scavenging (trace) elements and the insights that can be gained from complementary mineralogical and element analyses for the interpretation of trace element dynamics in waste rock.
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 | 0.001 | 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".