Cross-Border Wind Engineering Contributions: ASCE 7 — A Case-in-Point
Bibliographic record
Abstract
Canadian researchers in the area of wind engineering have been members of the ASCE 7 Task Committee on Wind Loads since its inception. This close collaboration among engineers across the border has helped establishing strongly shared research knowledge apparent in its application to the development and evolution of wind load provisions in ASCE 7, one of the most comprehensive wind standards in the world with 100 pages in its current version. The Task Committee is presently working towards the development of ASCE 7-10 updating the provisions of ASCE 7-05. The work of the Committee has been extremely important since ASCE 7, although a standard on its own, seems to have evolved to a "unified" code since other codes in the U.S. explicitly refer to and follow its provisions. The paper will glance at a number of issues the Committee is working at present and will concentrate on some issues that will eventually be addressed by the Committee. Research work relevant to current wind provisions and difficulties with the interpretation of others are the main forces motivating these changes. However, since this paper refers only to work in progress, a strong disclaimer regarding the final outcome of deliberations and voted decisions about changes in the wind load provisions of Chapter 6 is in order.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".