Monitoring and post‐occupancy evalution of Net<scp>ZEBs</scp>
Bibliographic record
Abstract
In Section Unavailable the motivations and reasons for monitoring building energy and comfort are presented from the point of views of building manager and building occupants. The energy performance of the 30 Task 40 Case Study buildings is presented in Section Unavailable . In Section Unavailable a detailed description of monitoring Net ZEBs throughout the various phases of a building project lifetime is presented spanning four building phases: Planning, Design, Installation and Operation. In Sections Unavailable and Unavailable two protocols for monitoring building energy performance and Indoor Environmental Quality (IEQ) are described. IEQ monitoring includes Post-Occupancy Evaluation (POE) of thermal comfort, lighting comfort, acoustic comfort and air quality. Four levels of energy monitoring and three levels of assessing IEQ and comfort are presented, representing the most basic to the most fully developed monitoring procedures. The energy and IEQ monitoring systems at the ENERPOS building, Reunion Island are presented as examples and discussed in detail. The last Section Unavailable consists of feedback from several Net ZEBs monitoring systems from which recommendations for achieving a reliable monitoring system are compiled.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".