Remediation Opportunities for Arsenic-Contaminated Gold Mine Waste
Bibliographic record
Abstract
Arsenic (As)-contaminated gold mine waste is a global problem and poses a significant risk to the ecosystem and community (e.g., carcinogenic, toxicity). Arsenic concentrations of 77,000 mg/kg and 22,000 mg/kg in mine waste have been reported for Canada and Australia, respectively. Research is investigating environmentally sustainable techniques to remediate As-rich mine waste. Biological techniques involving plants (phytoremediation) and soil amendments have been studied to bioaccumulate As from soil (phytoextraction) or stabilise As in the rhizosphere (phytostabilisation). Identified plant species for phytoremediation are predominately fern species, which are problematic for arid to semi-arid climates, typical of gold mining areas. There is a need to identify native plant species that are compatible with arid to semi-arid climates. Arsenic is toxic to plants; therefore, it is vital to assess soil amendments and their ability to reduce toxicity, enhance plant growth, and improve soil conditions. The effectiveness of a soil-amending phytoremediation technique is dependent on soil properties, geochemical background, and As concentrations/speciation; hence, it is vital to use field soil. There is a lack of studies involving mine waste soil collected from the field. Future research is needed to design soil-amending phytoremediation techniques with site-specific mine waste soil and native plant species.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".