Performance of Small Evacuated Canisters Equipped with a Novel Flow Controller for the Collection of Personal Air Samples
Bibliographic record
Abstract
Small evacuated canisters have become more common in industrial hygiene personal sampling in recent years. The smaller canisters necessitate a low flow rate to ensure a full-shift air sample can be collected. Evaluation of small evacuated canisters compared to sorbent sampling methods is essential to ensure that the canisters accurately monitor airborne contamination. This data, in a controlled environment, will provide practitioners with valuable reference information when considering air-sampling campaigns. Six 300-mL evacuated canisters were used to collect 6-hour breathing zone samples of styrene on volunteers in a large exposure chamber. The canisters were specially designed with a capillary flow controller developed at McGill University in the mid-1990s. Based on the geometry of the capillary the airflow into the canisters was controlled to a low flow rate, approximately 0.3 mL/min. This low sampling flow rate allowed for the use of small-volume canisters as personal samplers to collect styrene vapors. Charcoal tubes and diffusive badges were simultaneously used to collect side-by-side samples for comparison. In addition, an online gas chromatograph (GC) documented the concentration in the chamber throughout the duration of the exposure. The three methods did not disclose any significant statistical difference when compared to the online GC values and to each other. In addition, linear regression analysis between the charcoal tubes and the canisters resulted in a correlation (R(2) > 0.95). An evaluation of the bias and precision (overall uncertainty) of the capillary-canister method, charcoal tubes, and diffusive badges found them to be within criteria established by European Committee for Standardization 482. The results indicate that the capillary-canister sampling device can be an acceptable alternative to sorbent samplers as a personal sampler providing reliable results that are representative of exposures.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".