Methanol exposure to car occupants from windshield washing fluid: a pilot study
Bibliographic record
Abstract
UNLABELLED: Automobile occupants might be exposed to considerable amounts of methanol from previously unreported source, namely via the inhalation of vapors of winter-grade, methanol-based, windshield washing fluid that drains to the intake air ducts of the car. Air samples were collected in passenger cars during simulated operating conditions and analyzed for methanol via headspace gas chromatography-mass spectrometry, electron impact, selected ion monitoring. The method was linear in the 2-2000 ppm range. Concentrations exceeding 1000 ppm were recorded. PRACTICAL IMPLICATIONS: Using a winter-grade, methanol-based, windshield washing fluid for windshield cleaning in a passenger car can result in a methanol concentration in the air of the passenger cabin in excess of 1000 ppm. In view of the widespread use of this product, more studies are necessary to elucidate, in depth, the concentrations of methanol vapors which could be encountered in various weather and driving conditions as well as the concomitant contributing influences of car design. These studies are necessary to properly assess the hazards associated with use of the fluid and possible mitigation approaches which might include substitution of methanol by less toxic formulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".