Thermal comfort assessment through measurements in a naturally ventilated LEED Gold building
Bibliographic record
Abstract
Reductions in electric power consumption at the University of Washington are an established sustainability performance target. In order to meet this target, Leadership in Energy & Environmental Design (LEED) certification of buildings on campus is part of a long term plan for the University. It has been assumed that LEED certification will result in less power usage by occupants while improving indoor environmental quality. However, the related indoor environmental quality for these certified buildings has not been evaluated in situ. The primary objective of our study was to investigate the indoor quality assessment, more specifically in this paper, we discuss the thermal comfort of a LEED Gold building through both in-situ measurements of temperature, humidity, and occupant comfort surveys. Three measurement stations have been implemented in a low-rise retrofitted Student Union Building starting April of 2014: two in a food court or commercial kitchen environment and the other in a small office. Surveys to assess the comfort levels of both populations have been undertaken. The resulting data set is rich in terms of providing technical and nontechnical feedback on the thermal comfort of a LEED certified building. Preliminary findings indicate that thermal comfort parameters employed for heating, ventilation and air-conditioning systems control were not optimum in practice.
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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 | 0.000 |
| 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) | 0.000 | 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".