The Controlled Active Ventilation Environment Laboratory (CAVE) – Resilient Buildings and Indoor Environments
Bibliographic record
Abstract
UCL’s CAVE is a new laboratory which just completed construction in East London, which specialises in urban fluid mechanics, air pollution, Indoor Air Quality (IAQ), and thermal comfort. The lab is designed to measure, quantify risks, provide data for model calibration and test solutions at full scale. CAVE is a complex climate- and ventilation-controlled indoor laboratory. With a plan area of 206 m2 and height of 9 m, CAVE’s large size allows fully monitored and full scale “living labs” to be built inside the laboratory space. The laboratory systems enable simultaneous creation of independent “interior” and “exterior” environments within the lab at a large temperature range (-5°C to 43°C) to reproduce a wide range of realistic climate and air quality scenarios. CAVE can be used to simulate indoor classroom environments to test impacts of building ventilation and climate control technologies or strategies, low-cost technologies and green infrastructure, for a wide range of climate conditions. The lab can enable people to participate in experiments so as to understand the user’s response to those in terms of usability, thermal comfort, social and cognitive impacts and wellbeing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".