A Review on Phytoremediation of Decommissioned Mines and Quarries in Ontario: A Sustainable Approach
Bibliographic record
Abstract
Abandoned pits and quarries in Ontario, Canada, are on the rise due to industrialization, leading to ecosystem disruption and soil contamination with pollutants such as cadmium, cobalt, nickel, and barium, which may leach into nearby water systems. Current rehabilitation processes are slow to initiate, and therefore, the site remains in a contaminated condition for years. Phytoremediation, which involves using plants to remove contaminants from soils, is receiving increased attention for cleaning up decommissioned mines. This type of rehabilitation is normally practiced in situ by hand-planted and managed vegetation chosen for the specific purpose of contaminant removal. This study investigated the phytoremediation potential of indigenous plants as local seed sources to rehabilitate decommissioned quarries in Ontario. This study also investigated the potential of native plants to naturalize in the disturbed areas, thus providing a natural clean-up of the contaminants. Thus, if successful, this process will also initiate the re-establishment of native wildlife in the area. Through a literature review, 74 plant species were identified as capable of remediating 20 contaminants often found on the decommissioned quarry sites. The results may help ecosystem managers to adopt environmentally sustainable strategies to clean up contaminated sites such as decommissioned mines and quarry areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".