Performance evaluation of proprietary drainage components and sheathing membranes when subjected to climate loads, task 5: defining exterior climate loads
Bibliographic record
Abstract
The first objective of this work was to determine the environmental loads to be used for testing wall configurations as part of the project “Performance Evaluation of Proprietary Drainage Components and Sheathing Membranes when Subjected to Climate Loads” heretofore known as A1-00030 (B1264). The appropriate wind-driven rain loads, expressed in terms of a combination of water spray rate and pressure difference, for key locations in Canada were determined. The second objective of the work was to provide the weather data for the hygrothermal simulation portion of the project; i.e. select Moisture Design Reference Years (MDRYs) for the simulation task. Appropriate climate data for this task was provided for the locations identified in first part of the work. After reviewing several published methods for selecting weather years for hygrothermal simulation a small comparison study was undertaken. It was concluded that the MI MEWS method was appropriate to use for this project. MI MEWS rankings were produced for all the years in the climate record for each location selected. Three MI MEWS years, wet (maximum), average (median), and dry (minimum), were generated and converted to an acceptable format for hygrothermal analysis. As a by-product of the task hygrothermal years using the other methods considered were also produced as well as a 10-year sequence of the most recent years for each location. A sample table of the data generated for a typical location is shown below. This information forms part of the climate database generated for the project.
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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".