Updating a diesel engine exhaust job-exposure matrix with published measurement data
Bibliographic record
Abstract
A job-exposure matrix (JEM) is a tool that can estimate diesel engine exhaust (DEE) exposures. JEMs based on expert judgment or measurement data are limited by the information available at the time of development. Over time, more information about hazardous exposures is understood through additional measurements and peer-reviewed publications. This study presents a systematic approach to updating an existing DEE JEM using published data to better reflect current scientific knowledge. The literature was searched for occupational exposure studies that measured DEE as elemental carbon (EC) between January 2010 and May 2022. Four-digit North American Industry Classification System (NAICS) 2002 and National Occupational Classification-Statistics (NOC-S) 2006 codes were assigned to each identified subgroup within the studies. EC exposures were categorized as low (0–10 µg/m3), moderate (10–20 µg/m3), or high (>20 µg/m3). Weighted arithmetic means were calculated for each industry-occupation intersection (IOI) identified in the literature. These means were used to adjust, or retain, the existing exposure level within the JEM cells using a decision tree based on the number of studies, workplace locations, and pooled sample size of the weighted mean. Concordance was measured between the updated JEM (Diesel Exhaust in Canada JEM (DEC-JEM)), the previous (existing) JEM, and the Canadian Job-Exposure Matrix (CANJEM). Thirty-seven studies were identified from the published literature reporting on 53 unique IOIs (20 NAICS and 34 NOC-S codes), including occupations in the mining, construction, and transportation industries. Exposure levels for 66% of identified IOIs increased, most in construction and mining. After the decision tree’s results were expanded to the full DEC-JEM, the exposure level of 486 IOIs (12.5% of DEC-JEM) and 286,710 workers (15.8% of DEE-exposed workers) increased. There was a significant correlation between qualitative exposure levels in the updated DEC-JEM and CANJEM (Kendall’s τ = 0.364, p < 0.001). This study describes a systematic approach to updating an existing JEM to incorporate new scientific knowledge. The updated DEC-JEM better reflects existing exposure knowledge in several industries, particularly construction. Future analyses include investigating its use as an exposure assessment tool in disease surveillance.
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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 | 0.115 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.059 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".