Comparison of Endotoxin and Particle Bounce in Marple Cascade Samplers With and Without Impaction Grease
Bibliographic record
Abstract
The health of persons engaged in agricultural activities are often related or associated with environmental exposures in their workplace. Accurately measuring, analyzing, and reporting these exposures is paramount to outcomes interpretation. This paper describes issues related to sampling air in poultry barns with a cascade impactor. Specifically, the authors describe how particle bounce can affect measurement outcomes and how the use of impaction grease can impact particle bounce and laboratory analyses such as endotoxin measurements. This project was designed to (1) study the effect of particle bounce in Marple cascade impactors that use polyvinyl chloride (PVC) filters; (2) to determine the effect of impaction grease on endotoxin assays when sampling poultry barn dust. A pilot study was undertaken utilizing six-stage Marple cascade impactors with PVC filters. Distortion of particulate size distributions and the effects of impaction grease on endotoxin analysis in samples of poultry dust distributed into a wind tunnel were studied. Although there was no significant difference in the overall dust concentration between utilizing impaction grease and not, there was a greater than 50% decrease in the mass median aerodynamic diameter (MMAD) values when impaction grease was not utilized. There was no difference in airborne endotoxin concentration or endotoxin MMAD between filters treated with impaction grease and those not treated. The results indicate that particle bounce should be a consideration when sampling poultry barn dust with Marple samplers containing PVC filters with no impaction grease. Careful consideration should be given to the utilization of impaction grease on PVC filters, which will undergo endotoxin analysis, as there is potential for interference, particularly if high or low levels of endotoxin are anticipated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".