Executive summary of research contributions related to Moisture Management of Exterior Wall Systems (MEWS): modeling, experiments, and benchmarking
Bibliographic record
Abstract
Uncontrolled moisture accumulation in a building envelope reduces the structural integrity of its components through a combination of mechanical, chemical and biological degradation. Damage induced by moisture ingress includes rotting of wood studs and wood-based sheathing, as well as other detrimental effects such as efflorescence and spalling of masonry, and rusting of fastening mechanisms. Over the past decade, a significant number of low-rise wood-frame residential buildings have been plagued with water penetration problems related to uncontrolled moisture accumulation in the building envelope. In the majority of cases, the reported problems are in coastal areas such as lower mainland of BC, Maritime Provinces and eastern US. Effective moisture control is essential for an acceptable service life of building envelope. Effective moisture control implies both, minimising moisture ingress to prevent ingress of moisture into the inner most fabric of the wall assemblies, and redirection of moisture to the exterior. The principal objective is not to allow any component within the system to stay 'too wet' for 'too long'. The challenge would be to qualify and quantify what is considered "too wet" and "too long"?
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".