The rainscreen principle described in Construction Technology Update No. 9 provides a design approach for controlling rain penetration into exterior walls.
Bibliographic record
Abstract
redistribution. The moisture load on a particular element of the faade also depends largely on how the exterior Designing Exterior Walls According to the Rainscreen Principle by W.C. Brown, G.A. Chown, G.F. Poirier and M.Z. Rousseau This CTU discusses the application of the rainscreen principle to the design of exterior walls, building on the concepts presented in Construction Technology Updates No. 9 and No. 17. Sources of Climate Data The National Building Code of Canada 1995 provides climate data such as maximum 15-minute and 24-hour rainfall, and total annual precipitation. Climate normals for Canadian locations are available at http://www.cmc.ec.gc.ca/climate/. Additional information, such as total annual rainfall and number of days with measurable rain, is also provided. Driving rain wind pressure (DRWP) values are tabulated in CSA Special Publication A440.1. (DRWP is the maximum `instantaneous' wind pressure, coincident with rainfall, that is likely to be exceeded once
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".