Exposure to Dust, Resin Acids, and Monoterpenes in Softwood Lumber Mills
Bibliographic record
Abstract
A study to assess exposure to potential respiratory hazards in a large lumber mill processing spruce (Picea engelmannii and glauca), pine (Pinus contorta), and fir (Abies lasiocarpa) used a random sampling strategy to assess exposures for all jobs in the sawmill, planer mills, and yard. Personal samples for inhalable particulate were collected to measure exposure to dust and resin acids (abietic acid and pimaric acid). To estimate wood dust exposure, rather than overall dust, the resin acid content within dust was used in combination with observations of job tasks and proximity to dust sources. Passive dosimeters were used to measure exposure to alpha-pinene, beta-pinene, delta3-carene, and other unidentified wood volatiles suspected to be monoterpenes. The GM of the 220 inhalable particulate samples was 1.0 mg/m3 whereas the mean abietic acid, pimaric acid, and estimated wood dust levels were 7.2 microg/m3, 0.6 microg/m3, and 0.5 mg/m3, respectively. The GMs of the 222 monoterpene samples were 0.1 mg/m3 for alpha-pinene, 0.3 mg/m3 for beta-pinene, 0.1 mg/m3 for delta3-carene, and 0.5 mg/m3 for the unidentified wood volatiles. Monoterpene exposures were much lower than those observed in other studies conducted in Sweden and Finland. The results of this exposure assessment highlight the importance of considering the content of airborne particulates in lumber mills as well as potential exposure to wood chemicals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".