Phytoextraction of Lead in Contaminated Soil─A Collaboration between Introductory Analytical Chemistry and Campus Farm
Bibliographic record
Abstract
A community-engaged learning experiment was developed for an introductory analytical chemistry course at the University of Toronto Scarborough in collaboration with Campus Farm. The experiment introduced students to the application of analytical techniques to studying environmental samples. More specifically, students investigated the potential of red clover to serve as a hyperaccumulator of lead (Pb) while learning about broader phytoremediation strategies. The experience spanned two lab periods, the first involving a site visit to learn about the land and collect plant and soil samples. This was followed by a guided extraction and analysis of Pb content, introducing students to topics such as acid digestion, separation, and flame atomic absorption spectroscopy. Student results were aggregated and shared, allowing them to assess and comment on the efficacy of red clover to serve as a hyperaccumulator of Pb contamination. Moreover, student results and analyses were shared with community partners at the Campus Farm, with the opportunity to provide year-over-year data to monitor and assess a sustainable approach to remediating contaminated soil. Survey results reveal students’ appreciation for learning how analytical chemistry can be applied to study real-world samples and, more specifically, how it can be applied toward soil remediation efforts. This experiment can be adapted to work with students within the laboratory component of a course to assess a variety of other metal contaminants or study the efficacy of other potential hyperaccumulators moving forward, with the broader intention of informing and supporting soil remediation strategies in collaboration with community partners.
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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 | 0.001 |
| 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".