Six Month Tracking of Microbial Growth in a Metalworking Fluid After System Cleaning and Recharging
Bibliographic record
Abstract
Large volumes of metalworking fluids (MWFs) are used in manufacturing industries for cooling and lubrication of metal pieces and tools during machining. MWFs accumulate microbial growth through continuous recirculation and reuse. We studied the progression of microbial contamination for 6 months after dumping, cleaning and recharging (DCR) of a large semi-synthetic MWF system managed with several biocides. Fresh, uncontaminated fluid was added to the system after extensive cleaning. The following samples were collected and analyzed: pre-DCR fluid (before system cleaning); neat fluid diluted to 6% with water; in use MWF 12 h and 1, 3 and 6 months post-DCR. Samples were analyzed for total microorganism concentrations by direct counting using fluorescence microscopy and by plate counting on various media (R2A, BHI, Middlebrooks and rose bengal under aerobic conditions). In addition, PCR was performed for the detection of mycobacteria. There was a rapid progression in the total bacterial counts as determined by fluorescence microscopy: 5.7 x 10(7) cells/ml in the pre-DCR used fluid, no measurable bacteria in the neat fluid, 6.9 x 10(6) cells/ml after 12 h and 2.2 x 10(6), 3.6 x 10(8) and 6.1 x 10(8) cells/ml after 1, 3 and 6 months, respectively. On average, only 0.2% of the direct count organisms were quantified on R2A cultures. PCR showed the presence of mycobacteria in the used MWF at 3 and 6 months. Mycobacteria were also identified from cultures on Middlebrooks and R2A. This study demonstrates that standard methods for cleaning MWF systems are inadequate since residual bacteria in the system can rapidly repopulate the newly charged MWF.
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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 | 0.000 |
| 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) | 0.000 | 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".