Seasonal Indoor Humidity Levels of Apartment Suites in a Mild Coastal Climate
Bibliographic record
Abstract
It is essential to design and operate buildings with good indoor air quality because people spend most of their time indoors, and their productivity, comfort, and health depend on the quality of the indoor air. In addition to other indoor-air-quality parameters, the indoor humidity and temperature need to be controlled and maintained within acceptable ranges. Elevated indoor humidity creates favorable conditions for mold growth and building-envelope damage. To minimize such problems, it is important that designers have insight into the level of indoor humidity that will be expected in a building operating under a set of conditions and weather variation. In this paper, the results of monitoring the indoor temperature and humidity of four apartment suites with different occupancy levels are reported. Along with the indoor-air conditions, the local outdoor temperature and relative humidity were continuously measured for 17 months. The indoor humidities in the suites were correlated with the outdoor air temperature and humidity and compared with the European indoor climate class model. Moreover, the indoor-temperature and relative-humidity ranges in the four suites during the winter, spring, summer, and fall seasons and the temperature and humidity distributions within the suites are reported.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".