Evaluation of Bacterial Contamination and Control Methods in Soluble Metalworking Fluids
Bibliographic record
Abstract
In the United States, 1.2 million workers are exposed to metalworking fluids. During operations, aerosols are produced and airborne contaminants can be inhaled. Although biocides are used to control the bacterial content of metalworking fluids, they can create health-related problems, and their efficiency remains to be proved. The objectives of this project were (1) to verify whether rigorous cleaning according to a standard protocol could reduce microbial contamination and (2) whether the use of biocides with different spectra could reduce the bacterial population. Four similar machines producing similar components were evaluated; a specific treatment was applied to each machine. The machine used as a control (1) was thoroughly cleaned prior to sampling, (2) did not undergo any major cleaning afterward, and (3) was operated without the use of any biocide. A major cleaning is a protocol described and recommended by the fluid manufacturer and was performed on the three other machines, two of which were subsequently treated with biocides weekly. Fluid samples from the four lathes were collected weekly during a 6-month period, and total bacterial and cultivable Gram-negative bacteria were analyzed for each sample. Major cleaning of the machines (120-4) did not significantly reduce the concentration of bacteria in the cutting fluids when compared with the control machine (120-3), which had not undergone major cleaning. The concentrations of total bacteria were in the 10(6) CFU/mL range for these two lathes; however, a reduction in the total number of fluid changes was observed for this machine. Bacterial flora in the cutting fluids was significantly controlled with the use of biocides. Bacteria concentrations were in the 10(3)-10(5) CFU/mL range for the lathes with the use of biocides. Since thorough cleaning is insufficient and biocides are recognized as being responsible for some worker health problems, other avenues for controlling bacterial flora in cutting fluids should be evaluated to reduce worker exposure to their bacterial contaminants.
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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 | 0.003 | 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 | 0.000 | 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 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".