Silicone Wristbands as Personal Passive Samplers: Assessing Exposure to Organophosphate Flame Retardants
Bibliographic record
Abstract
Organophosphate flame retardants (OPFRs) are used as additives in a variety of industrial and commercial products, such as furniture and electronics, to meet fire resistance standards. However, OPFRs have been associated with health effects, including neurotoxicity, hormonal changes, and cancer; therefore, insight into levels of exposure is necessary for health and risk assessments. An individual’s exposure to OPFRs can be measured using silicone wristbands as passive samplers, which make use of the tendency for a chemical to equilibrate between the solid phase (the sampler) and air to measure personal exposure to contaminants over time. Because OPFRs are frequently used in electronics, this study aimed to examine correlations between electronic use and personal exposure to OPFRs. Silicone wristbands were cleaned to remove surface contaminants, and then deployed to participants to wear for one week. Following deployment, the adhered compounds were extracted and analyzed using gas chromatography-mass spectrometry. While deployed, participants filled out a questionnaire indicating their weekly electronic use, which was used to draw correlations between electronics and exposure to OPFRs. The median concentration of OPFRs on wristbands were found to be about 6300ng/wristband, with high variability between individual wristbands. This project will be one of the first in Canada to use silicone wristbands as personal samplers to address exposure to OPFRs in relation to electronic use, and thus will prove useful in risk assessment due to the increasing use of electronics in society. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Matthew Ross Department: Chemistry
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".