Deposition Patterns of Non-Exhaust Emissions on Taraxacum Officinale as a Potential Urban Biomonitor
Bibliographic record
Abstract
This study explores the potential of the common dandelion, Taraxacum officinale, to function as a biomonitor for non-exhaust emissions from brake pads and tire wear across 43 city parks in Mississauga, Ontario. The main objectives are to analyze the relationship between the length of different road classes (local, major and express) in a 1km buffer around each sampling site. As well as, explore the relationship between the distance to the closest road class from each study site and their relation to the deposition of trace metals on the surrounding flowers and seeds of the collected dandelions. To achieve these objectives, a trace metal analysis is conducted to determine the elemental concentrations on the dandelion samples in micrograms per gram of collected plant material. The mean trace metal concentrations are calculated for Zn, As, Se, Sr, Cd and Pb and their mean concentrations are 31.84 μg/g, 0.05 μg/g, 0.15 μg/g, 17.47 μg/g, 0.05 μg/g, and 0.27 μg/g, respectively. There is a statistically significant relationship between Zn and Pb concentrations on the seed samples for the length of local, major and express roads within a 1km buffer around each study site. The relationship between Sr on seed samples and Zn on flower samples with the closest local, major, and express roads is also statistically significant. However, the remaining relationships do not present a statistically significant association as there is not sufficient and consistent evidence to validate the effective use of dandelions as biomonitors for trace metals in Mississauga, Ontario.
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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 | 0.001 | 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".