Bibliographic record
Abstract
Moisture engineering is becoming an important task in the overall design of building enclosures in both North America and Europe. Several methods may be used to design wall systems, and modeling is definitively the most flexible approach. There is an increasing demand for calculation methods to assess the moisture behavior of building components. In North America alone, the estimated cost in increased energy consumption due to the presence of moisture is approximately $1 billion dollars annually. Current tasks, such as preserving historical buildings or restoring and insulating existing buildings are closely related to the moisture tolerance in a building structure. Calculative analyses are becoming increasingly important due to the expensive and time-consuming experimental investigations and the limited transferability to real situations. The Oak Ridge National Laboratory (Building Technology Center) and the Fraunhofer Institute for Building Physics in an international collaboration h ave jointly developed a moisture engineering assessment model that predicts the transient transport of heat and moisture. This model, WUFI-ORNL/IBP is now available in North America free of charge, and can be downloaded via the Internet at: www.ornl.gov/btc/moisture. The unique features of this particular model are that it incorporates vapor and diffusion transport mechanism, along with realistic boundary conditions that include wind-driven rain. This alone may account for more than 80% of the total moisture load in envelopes. In addition this model is tailored to North American materials and construction practices and has a very friendly user interface that appeals to both architects and engineers. The model is also the most benchmarked hygrothermal model developed, since 1994. In this paper a brief description of the model will be given showing all needed inputs for a brick wall envelope system located in Montreal CANADA.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".