Green Buildings Project Terms of Reference Background
Bibliographic record
Abstract
The advancement of green building practices was identified at the Regional District of Nanaimo Saturday, April 3, 2004 Sustainability Workshop 1 as one of the possible ways of making the region a more sustainable place to live. What is a Green Building? Green buildings are buildings that require less energy to operate, contribute fewer emissions to the environment, conserve water, generate less solid waste, and provide more comfortable and productive environments for their inhabitants. The definition of what is a green building is somewhat subjective, but different rating systems have been developed and are becoming more common in their use to assess a building’s greenness. Leadership in Energy and Environmental Design (LEED) appears to be the most well-known and widely used system to rate the greenness of buildings. It is a voluntary, consensus based self-assessment tool that has been embraced nationally and internationally as ‘the green building design standard 2 ’. LEED establishes a system in which a specified number of points are assigned according to the particular attributes of the building in five performance areas: the sustainability of the building site, water efficiency, energy and atmosphere, materials and resources, and indoor environmental quality. Depending upon the number of points accumulated, and hence the sustainability of the building, buildings are classified as LEED Certified, Silver, Gold or Platinum.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.206 | 0.134 |
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".