A novel dataset of indoor environmental conditions in work-from-home settings
Bibliographic record
Abstract
During the last week of March 2020, millions of workers around the world transitioned to working from home due to the COVID-19 pandemic. While much research has explored the behavioural and psychological aspects of work-from-home (WFH), few studies have provided data-driven evaluations of indoor environmental quality (IEQ). This paper presents findings from a summer 2022 field study of IEQ conditions in WFH settings, involving 95 participants in the North American Pacific Northwest. Sensors installed on participants' work desks continuously measured total volatile organic compounds ( t VOC), particulate matter (PM 2.5 ), carbon dioxide (CO 2 ) air temperature, humidity, and sound pressure levels (SPLs). Participants also completed surveys on home and workspace characteristics, as well as their subjective assessments of IEQ, well-being, and productivity. The study found that mean indoor concentrations of t VOC, PM 2.5 , and CO 2 were 262 ppb, 5 µg/m 3 , and 712 ppm, respectively. Indoor air temperature ranged between 14.7–32.3 °C, with a mean of 22.8 °C, while relative humidity and SPL averaged at 52.5 % and 53.7 dBA. Statistically-significant associations were observed between IEQ variables and factors such as residence type, cooking habits, workspace type, and window availability. A future paper will assess the surveyed behavioural and psychological characteristics in greater detail.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".