The Robert L. Preger Intelligent Workplace<sup><scp>TM</scp></sup>a Transformative Living Laboratory at Carnegie Mellon University
Bibliographic record
Abstract
Climate change, rapid advances in technology, and the global pandemic have significantly changed the nature of work and the workplaces that are best suited to ensuring a healthy, productive workforce. The Robert L. Preger Intelligent Workplace™ (IW) is a 700 m 2 living laboratory of component and subsystem innovations in an occupied, lived-in laboratory with integrated passive-active systems for sustainability. The IW enables the interchangeability and side-by-side demonstrations of innovations in HVAC, enclosure, lighting, interior, networking and control components and assemblies. Every Architecture, Building Science, and Building Engineering program should have an IW, a living laboratory of innovative component and subsystem, and a testing ground for the next generation of building systems integration for indoor environmental quality and resource sustainability. The “living laboratories” should be seen as scientific instruments to rival engineering and science labs, support ongoing collaboration with the building industry for research and education of a new generation of building scientists and FM practitioners.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.335 | 0.113 |
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".