Feasibility of using Pleurozium schreberi as a biomonitor to study antiozonant dispersion: A case study in Southern Quebec
Bibliographic record
Abstract
p-Phenylenediamine antioxidants (PPDs), widely used as additives in tires and rubber, are released into the environment through tire and road wear particles. These compounds undergo oxidative processes, forming quinone derivatives that pose significant environmental and health risks, particularly to aquatic organisms (EPA, 2024). While runoff has been identified as the primary transport mechanism, the atmospheric dispersion of PPDs has received less attention. Bryophytes have been widely used as biomonitors of airborne contaminants and atmospheric deposition. Using a biomonitoring approach, this study investigated the atmospheric deposition of PPD antiozonants, including 6PPD, 6PPDQ, and DPPD, across Southern Quebec (Canada), a region characterized by the highest population density and pollution levels in the province. Samples of Pleurozium schreberi , a common species used for biomonitoring of atmospheric deposition, were collected on three site types with varying degrees of traffic exposure: roadsides, parks/playgrounds, and non-urban areas. Our findings demonstrated atmospheric dispersion of PPDs throughout Southern Quebec with a decreasing trend in total concentrations of PPDs with increasing distance from traffic. 6PPDQ was the most frequently detected compound, ranging from <LOQ to 3.71 ng g −1 . DPPD, the least detected, ranged from
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".