Towards Development of a Performance Standard for Assessing the Effectiveness of Wall-Window Interface Details to Manage Rainwater Intrusion
Bibliographic record
Abstract
Laboratory water spray testing identifies the performance of a component or assembly under a specified set of simulated wind-driven rain conditions. Well-developed water spray test protocols can also help identify where an assembly is vulnerable to water entry, the test loads at which water entry occurs, and whether the water entry is managed by the installation details in such a way that it does not result in within-wall damage. This paper presents a proposed laboratory test protocol for assessing the effectiveness of wall-window interface details with regard to management of rainwater and provides a rationale for a performance-based approach to the evaluation method. An overview of the test approach is provided, and details of the test apparatus and test specimen are given, including information on implementation of the test method. Examples of testing performed according to the proposed protocol are provided. Finally additional tests for evaluating the performance of installation details are suggested. The additional tests are for field evaluation of installation details and for laboratory evaluation of installation details with regard to the risk of condensation along window frames.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".