Experimental Prototype and Measurement Driven Study of Indoor Air Quality
Bibliographic record
Abstract
People spend a considerable amount of time indoors than outdoors. Given the hot or cold climatic conditions that prevail in different geographies, most indoor environments tend to be heated/cooled for ensuring occupant comfort. The quality of air that people breathe in such indoor environments is therefore vital for their long term health. Whilst we hear a lot about air pollution in cities, there is seldom a talk about indoor air quality. This paper elaborates on a proof of concept prototype for air quality monitoring using open off-the-shelf components and highlights findings from measurements conducted using this prototype in buildings on a University campus. Measurements were conducted in classrooms in different buildings at different times of the day with different temperature, humidity levels in these premises. Findings from this study show that air quality is fairly stable and within the acceptable limits across the different measurement locations. One of the outliers was a small dorm room with many people present during the measurement duration which exhibited degradation in the air quality level. This highlights that in crowded indoor environments, it may be necessary to closely monitor air quality and provide the necessary countermeasures to improve it.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".