Sampling Method for Welding Fumes and Toxic Gases in Malaysian Small and Medium Enterprises (SMEs)
Bibliographic record
Abstract
In 2009 there were 28,840 small and medium enterprises (SMEs) in Malaysia which represented 94.2 % of the total establishments in the manufacturing sector. Job tasks in manufacturing sectors above all involve welding processes. The issues in SMEs mainly resolve around poor working conditions contributing to worker’s safety and health problem. Welding fumes and toxic gas assessment in SMEs welding workplace is essential in order to ensure the minimum level of exposure is maintained as required by the prevailing standards. This paper outlines the methodology for fumes and toxic gas sampling by taking into account analytical method currently available for analysis in the government accredited laboratory. The proposed methods are divided into two; the pilot test and the actual measurement. Standardize sampling method using sampling pump along with direct reading measurement are consider in both the pilot test and actual measurement. The proposed sampling method hopefully will benefit researcher, stakeholders or SMEs by giving guidance on the suitable method for welding workplace assessment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".