Cutting Fluids Usage and Impacts in Metal Workshops in Ibadan, Southwestern Nigeria
Bibliographic record
Abstract
This paper presents a report on cutting fluids utilization and its impacts on workers in machining workshops in Ibadan, Nigeria. The major users of cutting fluids and their workshops were identified. It was found that there are 103 operating metal workshops in Ibadan and these workshops are located within seven local government areas of the city. Out of the total number, 85 are in fabrication, 39 are in crankshaft operations and 32 are engaged in block boring operations. The type and consumption of cutting fluids, coolant delivery techniques, length of use before disposal, disposal methods, and monitoring maintenance were studied. The results indicated that the most used cutting fluid is soluble oil with average total consumption of 402 litres monthly. The pouring of spent cutting fluids on the ground is the most adopted disposal method. Research on occupation exposures to cutting fluids has suggested that machine operators in metal cutting are at high risk of developing cancer, allergenic disorders, and lung diseases. Results obtained also showed that less than 22% of machine operators were aware of occupational hazards. Few of the operators (23%) wore safety devices/clothing, and health and safety standards were neither practiced nor enforced.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".