Fluorinated fire-figthing foams: manufacture, applications, ecological consequences
Bibliographic record
Abstract
Information on the production and use of fluorine-containing foaming agents intended for foam extinguishing of fires with oils and other flammable liquids as well as ecological consequences are reviewed in the article. It is shown that poly- and/or perfluorinated compounds usage for fire-fighting foam production led to the emergence of a large group of hazardous chemicals in the environment, including perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS). General information about fluorine-containing foaming agents, their manufacturers and labelling, properties, possible volumes of production and application on a global scale are given. It is shown that the use of fire-fighting foam to extinguish fires, as well as during training, leads to direct discharges of PFOS, PFOA and other fluorine-containing compounds into the environment. The results of studies carried out in various EU countries, Norway, the USA, Canada and Australia, which testify to high concentrations of PFOS, PFOA and other fluoride-containing compounds in groundwater and surface waters as well as in soils, are discussed. The highest levels of contamination are found in the sites where fluorine-containing foaming agents are used. The danger of fluoride-containing compounds entering the human body with contaminated drinking water and food products is shown.
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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 | 0.001 |
| 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) | 0.003 | 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".