Phytoremediation of road salt affected soils in the Toronto region using native halophytes
Bibliographic record
Abstract
Freshwater salinization is a growing concern in areas of the world experiencing freezing winter temperatures. Winter salt applied to roads can flow directly to surface water and groundwater, but can also be stored in roadside soil for months or years after application. Halophytes (i.e. salt-tolerant plants) remove excess salt from soil and can assist with remediation of saline soils. This study aimed to determine the ability of native halophytes to remediate road salt contaminated sites over two growing seasons. Four halophytes native to the Toronto, Canada region were planted in experimental plots next to a road that receives salt in the winter months. Two accumulator halophytes ( Panicum virgatum and Sporobolus cryptandrus ) and two recretohalophytes ( Sporobolus michauxianus and Bouteloua curtipendula ) were selected. Each species was planted in its own 2 × 6 m plot according to literature recommended planting densities. Plant and soil samples were collected at the end of each growing season to evaluate the amount of salt removed from soil and the amount of salt taken up by the plants. The accumulator, P. virgatum , and the recretohalophyte, S. michauxianus demonstrated the greatest potential for short remediation timelines (< 2 years after their second growing season). S. michauxianus and P. virgatum extracted approximately 22,000 mg/m 2 and 17,000 mg/m 2 of chloride over a single growing season, respectively. This is the first multi-year in situ study to examine the potential for halophytes to remediate road salt contamination. Halophytes provide a unique nature-based solution for the remediation and mitigation of road salt contamination .
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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 | 0.000 | 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 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".