Technical, Occupational Health and Environmental Aspects of Metal Degreasing with Aqueous Cleaners
Bibliographic record
Abstract
Aqueous cleaners used for metal degreasing are detergent formulations containing surfactants (such as linear alkylbenzene sulphonates, alkylphenol ethoxylates or alcohol ethoxylates), builders (such as hydroxides, phosphates or silicates), sequestrants (such as EDTA or NTA), anti-corrosive agents (such as ethanolamines), solvents (such as glycol ethers or d-limonene) and other specialty additives. Generally sold as concentrates, they are typically diluted between 3 and 20 times in water, leading to solutions containing only a few per cent active products. The cleaning efficiency depends on physicochemical phenomena such as wetting, solubilization, emulsification, dispersion, sequestration and saponification, and is enhanced by thermal and mechanical energy. Cleaning equipment is based on spraying or immersion of the parts and may include drying and rinsing steps. Because of the complexity and variability of the mixtures, the occupational health and environmental evaluation of aqueous cleaners is based on the study of their components. Aqueous cleaners are generally believed to present a low risk to workers' health and to the environment. However, some anionic surfactants and strong alkalis are skin and eye irritants, ethanolamines are allergenic and several glycol ethers of the ethylene glycol family are proven systemic toxicants that are easily absorbed through the skin. Although most components of aqueous cleaners are biodegradable and of low ecotoxicity, alkylphenol ethoxylates degrade into persistent and toxic compounds. Phosphates, if released directly into the environment, may cause eutrophication of rivers and lakes. Waste recycling or treatment by specialized facilities is usually required for spent solutions containing contaminants such as oils and heavy metals. From a technical, toxicological and environmental standpoint, aqueous cleaners can be used successfully to replace traditional organic solvents used in metal degreasing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".