Identifying the Determinants of Viable Microorganisms in the Air and Bulk Metalworking Fluids
Bibliographic record
Abstract
Exposure assessment was conducted for an epidemiologic study of the respiratory effects of exposure to metalworking fluids (MWF). As part of the study, airborne microorganisms were collected with a two-stage microbial impactor, and a sample of the bulk soluble MWF was collected from each machine sump, as well as information about the work environment. These data were then used to develop multivariate statistical models of the determinants bulk MWF and airborne microbial levels. Microbial concentrations in the bulk MWF ranged from 5 x 10(4) to 5 x 10(10) colony-forming units (CFU)/mL, with a geometric mean of 3.4 x 10(7) CFU/mL. The geometric mean airborne microbial level was 182 CFU/m3 (for particles size <8 microm) with a range of 1 to 8,308 CFU/m3. In modeling the determinants of bulk microorganisms, fluid-related factors were the most important characteristics associated with microbial levels, followed by process-related and environmental factors. The final full multivariate model predicted a significant reduction in bulk microbial levels by increasing pH of the fluid and reducing the amount of tramp oil leaking into the fluid. For the airborne microbial models, process-related factors were the major characteristics associated with microbial levels, followed by factors related to worker activities and environmental factors. The final full multivariate model predicted a significant control of airborne microorganisms by increasing worker distance from the machine, reducing the number of machines within 10 feet of the worker, decreasing the bulk microbial levels, and adding machine enclosures. These models can be used to prioritize nonbiocidal interventions to control microbial contamination of the bulk MWF and the air.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".