A Model for Predicting Endotoxin Concentrations in Metalworking Fluid Sumps in Small Machine Shops
Bibliographic record
Abstract
METHODS: In British Columbia, Canada, nineteen small machine shops which used water-based metalworking fluids (MWF) were examined. One bulk MWF sample was taken from each independent sump (N=140) and tested for endotoxin using the Limulus Amoebocyte Lysate assay. Factors that might influence the MWF sump endotoxin concentration were investigated using mixed effect multiple regression modelling to control for repeated measures within shops. RESULTS: The geometric mean (GM) endotoxin concentration was 6791 EU/ml. Contamination of MWF with tramp oil, MWF pH, MWF temperature, and MWF type were significant predictors of sump fluid endotoxin concentration (model P=0.0001, ordinary least squares R(2) =0.36). Concentrations of endotoxin in sump fluids were increased by MWF contamination with tramp oils such as hydraulic oils, preservative oils, spindle oils, slidway lubricants, gear lubricants, and greases (model predicted GM=17400 EU/ml vs. 1600 EU/ml without tramp oil). Concentrations were also elevated where pH was lower than 8.5 (predicted GM=10600, vs 3600 EU/ml for pH 8.5 to 9.5), where soluble fluids were used (predicted GM=11800 vs. 2800 EU/ml for synthetic fluids), and where sump fluid temperatures were higher (predicted GM=2600 EU/ml at 11 degrees C vs. 21500 EU/ml at 32 degrees C). The within-shop correlation of sump bulk fluid endotoxin concentrations was 38%. CONCLUSIONS: Minimizing tramp oil contamination, using synthetic fluids, and monitoring pH and temperature would be valuable tools for controlling endotoxin contamination in MWF sumps. In addition, since there was correlation within-shop, contamination of one sump in a shop may suggest changing the fluids in all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".