Improve energy and environmental efficiency of a generic office building
Bibliographic record
Abstract
A world wide effort has been made to promote green measures among office buildings, as this building sector consumes a substantial amount of energy and contributes to Green House Gases (GHG) emissions. For example, commercial and institutional buildings consumed 6% of Canada's secondary energy use in 2001 (NRCan, 2002, 2007). This study was geared at exploring the applications of low energy technologies in office buildings. Energy and resource saving measures were extracted through a review of 55 energy efficient buildings from a wide range of climates, building sizes, and occupancy densities. The most commonly used measures were applied to a synthetic office building. The baseline building was conceived to meet the requirements of the Canadian Model National Energy Code for Buildings (MNECB) (NRC, 1997). Trnsys and the Comprehensive Assessment System for Building Environmental Efficiency (CASBEE) from Japan were used to investigate the energy performance and eco-efficiency of this building. The final results demonstrated that, by applying those commonly used energy saving measures, the annual energy use of the building was reduced by 82%. More interestingly, the eco-efficiency ranking of the building was increased from B+ (good) to A (very good) only by integrating a ground source heat pump (GSHP) into the building energy system.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".