Bibliographic record
Abstract
Air leakage in commercial buildings in the U.S. accounts for about one quad of energy annually. As the thermal resistance of commercial building envelopes continues to improve, the relative contribution of air leakage to heating and cooling loads is increasing. Several manufacturers have developed advance air barrier technologies and construction practices to reduce the air leakage in buildings. To help in the market penetration of these advance technologies, advances in easy to use tools for determining the impact of air leakage are needed. Oak Ridge National Laboratory (ORNL), the Air Barrier Association of America, the National Institute of Standards and Technology, and the US-China Clean Energy Research Center for Building Energy Efficiency partnered to develop an online calculator that estimates the potential energy and cost savings in major US, Canadian, and Chinese cities from improvements in airtightness. The calculator estimates the energy and cost savings potential based on the pre and post retrofit air leakage rates for prototype commercial buildings. This report explores extending these savings to determine the national level energy savings potential based on the weightage of commercial building type in different ASHRAE climate zone locations. The base infiltration rate of 1.07 CFM/ft2 was assumed for these calculations and the savings were calculated for three different air infiltration target rates of 0.4 CFM/ft2, 0.25 CFM/ft2, and 0.06 CFM/ft2. The national source energy savings of 238, 283, and 313 TBtu respectively were estimated for these target air infiltration rates.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".