Which is the weakest link? Wind performance of mechanically attached systems
Bibliographic record
Abstract
Wind dynamics, on a mechanically attached single ply roofing assembly, lift the membrane and cause fluttering, introducing stresses at the attachment locations. To identify the component of the system that has the weakest resistance against wind uplift forces, a dynamic method of evaluating roofing systems is beneficial. Each component in the assembly offers resistance to wind uplift. Failure occurs when the wind uplift force is greater than the resistance of any one or more of these links. This study presents data that will help roof system designers maximize wind uplift ratings by choosing the appropriate roof components at the early design stage or by replacing/adding components to improve wind uplift resistance during reroofing. By diagnosing the weakest link from the failure mode, one can improve the wind resistance of a system. Therefore, a series of Mechanically Attached Systems (MAS) were constructed and exposed to simulated dynamic wind uplift forces at the Dynamic Roofing Facility of the National Research Council Canada (NRC-IRC). The goal of the paper is to present the ongoing experimental program and answer the following questions:
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".