Development of a quantitative job exposure matrix for endotoxin exposure in agriculture
Bibliographic record
Abstract
Objective To develop a quantitative job exposure matrix (JEM) for the assessment of endotoxin exposure among farmers and other agricultural industry workers. Methods An exposure database containing 3,384 personal endotoxin measurements from Western European and Canadian workers employed in animal and crop production and related-industries with endotoxin exposure between 1992 and 2008 was established. Data were log-transformed and modelled with linear mixed effect models where job-titles, company (within job-titles) and worker (within company) identities were treated as random effects. Fixed effects were year and season of measurement, sampling duration and an exposure prior (none, low, high) for every job code (ISCO-68) from an existing JEM for general population studies. Results The model results suggested overall levels of endotoxin exposure to decline annually by almost 2%. Season was a strong determinant of endotoxin exposure with measured concentrations being higher during the winter (b = 0.64; p <.0001) compared to the summer. Effects of sampling duration on the exposure were rather small. Predicted exposure levels were highest among wheat, vegetable, crop and poultry farmers and lowest among nursery garden workers, gardeners and horticulture farmers. Derived exposure estimates showed good agreement with endotoxin levels reported in the literature and not included in the database. Perspectives The model predictions will be used to develop a quantitative JEM with a time axis for endotoxin exposure to be used in epidemiological studies among farmers and agricultural industry workers.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".