Particulate matter and carbon dioxide monitoring in indoor places
Bibliographic record
Abstract
People spend most of their time in enclosed spaces (e.g., apartments, offices and public buildings).According to research, indoor air quality can be worse than the outdoor air quality (OAQ).Hazardous chemicals found in the air indoors can adversely affect the functioning of the human body and cause many respiratory and circulatory diseases.Little is known about particulate matter (PM) concentration in indoor space of various services and office buildings/facilities (not related to production, i.e. offices, shops, kitchens etc.) and its associated health risk.Similarly, carbon dioxide (CO 2 ) is a gas scarcely analyzed in the buildings but it is a good marker of living comfort.In this study, measurements of PM and CO 2 were taken in indoor environments, poorly investigated in the past.The concentrations of PM were monitored and compared using a GRIMM analyzer model 1.108 in two offices, two printer rooms and two bedrooms, while the levels of CO 2 were investigated by means of a Sensordrone low-cost multi-sensor in a computer-room, in addition to the same offices and bedrooms already under study.The indoor PM concentration was certainly influenced by outdoor levels but human activities played a key role causing a worsening of indoor air quality.The concentration decrease rates of fine particles (the most harmful) were lower than those of the coarse fraction; therefore, the effects were still appearing even after the end of the activities that generated it.According to the latest guidelines, the average concentrations of CO 2 measured between 990 ppm and 1,318 ppm suggested a low standard of comfort of building occupants, which may suffer from headaches, drowsiness and attention deficit.In recent years, the portable sensors have produced a great potential in creating extended monitoring networks in real time; however, a progress in reliability of data is needed.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".