Field Evaluation of a Displacement Ventilation System for a Cold Climate School
Bibliographic record
Abstract
Displacement ventilation (DV) is believed to provide better indoor air quality for a given outdoor air flow rate. Few reports of field assessments of DV have been published, especially for cold climates. A post-occupancy study of DV performance was conducted at Lawrence Grassi Middle School (LGMS), located within Alberta’s cold-dry climate. The DV performance evaluation addressed vertical temperature profile, ventilation effectiveness (VE), and thermal comfort in five spaces (three classrooms, the computer lab, and the library) during three different seasons. This study included testing of parameters that may affect DV performance such as: door position, season, occupancy density, thermal loads, ventilation rate, and radiant surfaces temperature. Field evaluation suggested that DV could provide improved thermal comfort and VE compared to conventional (i.e., mixing ventilation) systems when operated as prescribed in the literature. At LGMS, performance indices clearly showed that DV in classrooms was functioning as one would expect. The library had a clear short-circuit due to high supply air discharge temperature and a reversed temperature profile. The computer lab showed similarity to typical DV performance, with lower VE and cooler thermal environment than the classrooms. Thermal comfort indices reflected an overall thermal environment that was cooler than neutral, especially near the floor. This was largely due to a radiant slab colder than comfort limits. Thermal comfort indices leaned toward the cooler edge of comfort limits. Comparing spaces, the classrooms had the best comfort levels, followed by the computer lab and then the library.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".