Bibliographic record
Abstract
Although they share a common goal of promoting more sustainable design and construction practices, the Envision and Leadership in Energy and Environmental Design (LEED) sustainability rating and certification frameworks come from separate backgrounds and their ‘roots’ are reflected in how they are practically applied to projects. LEED was conceived to guide green building design based on occupant comfort and energy efficiency. Envision, in contrast, is focused more on the extended impacts that infrastructure projects can have on communities. Each certification framework hypothetically applies to distinct types of projects, with LEED reserved for occupied buildings and Envision reserved for non-occupied infrastructure projects. In practice, however the distinction is often blurry. Public infrastructure assets such as water and wastewater pump and treatment facilities often include spaces that are temporarily occupied yet experience different challenges in design from what either framework was conceived to address. Some owners have found value in drawing on the guidance in both LEED and Envision to achieve their performance goals for their assets. By examining infrastructure projects that have sought dual certification under both LEED and Envision, this paper will explore the relative benefits and limitations inherent in each framework, and areas where there are overlap and synergy in guidance provided. This paper will seek to answer the question of how these tools complement one another, and what value practitioners using each framework in isolation could learn from critically examining the other.
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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".