Occurrence of toluene in Canadian total diet foods and its significance to overall human exposure
Bibliographic record
Abstract
Levels of most VOCs in foods are usually low because of their volatility, and human exposure to VOCs is expected to be mainly via inhalation of ambient and indoor air. However, dietary exposures to VOCs can be significant to overall exposures if elevated concentrations of VOCs are present in foods consumed in high amounts and/or on a regular basis, and this was demonstrated in this study with the occurrence data of toluene from the recent 2014 Canadian Total Diet Study (TDS). Concentrations of toluene in the composite samples of most food types from the 2014 TDS are low and similar to the results from the previous 2007 TDS with some exceptions, such as beef steak (670 ng/g (2014 TDS) vs. 14 ng/g (2007 TDS)), poultry, chicken and turkey (307 ng/g (2014 TDS) vs. 8.8 ng/g (2007 TDS)). Toluene concentrations in most of the grain-based and fast food composite samples from the 2014 TDS are considerably higher than those from the 2007 TDS, with the highest level of 4655 ng/g found in the composite sample of crackers from the 2014 TDS (compared to 18 ng/g from 2007 TDS). Dietary exposure estimates for toluene based on the occurrence results from the 2014 TDS show that for most of the age groups, grain-based foods are the primary source, accounting for an average of 77.5% of the overall toluene intake from the diet. The highest dietary exposures to toluene were observed for the adult age groups, with estimated average exposures ranging from 177.4 to 184.5 µg/d. Dietary exposure estimates to toluene are well below oral doses associated with toxicological effects and also below the maximum estimated intake (819 µg/d) from air inhalation for adult group (20 - 70 years) based on the results from CEPA (Canadian Environmental Protection Act) assessment in 1992.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".