Study of effects of mining industry contaminations on protective properties of arc rated clothing using ASTM F1959
Bibliographic record
Abstract
Arc rated FR protective clothing and fabrics are most often tested in new and laundered condition to remove surface contaminants. Manufacturers recommend to keep such clothing in clean condition at all times. However through daily use, protective clothing may be soiled or stained with industrial contaminants. The effect of specific mining industry contaminants on protective properties of arc rated flame resistant fabric is described in this report. A method and approach to quantify the contamination for a number of different contaminants was developed and described herein. The baseline contaminant was a 1% saline solution to simulate body sweat. It is generally accepted that water absorbed into the fabric will change the insulation and protective characteristics of the fabric. In this work, other contaminants used included diesel fuel (to represent lighter hydrocarbons such as gasoline and other lighter fuel oils), dry lime, nickel dust palladium dust, sodium hydroxide slurry, carbon black dust, dry and wet cement, a flocculent slime, hydraulic fluid (hydraulic jack oil) and transformer oil. Reduction in protective properties of arc rated FR clothing was observed for wet contaminations. Dry contaminants have less of an affect and some improved the protections level. Conductivity of the cloth was also measured using a high voltage meg-ohmmeter. These readings were performed with and without contaminants applied.
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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 | 0.001 |
| 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 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".