Domestic activities and pm generation: a contribution to the understanding of indoor sources of air pollution
Bibliographic record
Abstract
Few researches on domestic indoor air pollution have given quantitative information on the variation of the characteristics of the indoor source of particulate matter (PM).The purpose of this work was to investigate the emission characteristics of the common indoor particle sources.More specifically, this paper is intended to contribute to the understanding of how normal domestic activities could affect the human health.The emission sources of PM studied in this work was cooking, vacuuming, ironing and the use of deodorant spray.Cooking activities were expected to be one of the major sources of indoor particles and the features of the cookers may affect the characteristics of emissions resulting from cooking.For this reason, the results of a previous study based on the variability of the power of the cooker were reported, to study the sustainability of natural gas from the point of view of the induced indoor human exposure to PM.Measurements were performed by a GRIMM analyser, able to measure 16 granulometric classes from 0.3 to 20 µm.The study found that the activity of cooking and the use of deodorant spray are the sources that produce the higher indoor concentration levels compared with the other sources studied.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".