DISCUSSION ON "CANADIAN BRIDGE DESIGN CODE PROVISIONS FOR FIBER-REINFORCED STRUCTURES" BY BAIDAR BAKHT, GEORGE AL-BAZI, NEMY BANTHIA, MOE CHEUNG, MARIE-ANNE ERKI, MARTIN FAORO, ATSUHIKO MACHIDA, AFTAB A. MUFTI, KENNETH W. NEALE, AND GAMIL TADROS
Bibliographic record
Abstract
A discussion of an article with the aforementioned title, by Baidar Bakht, George Al-Bazi, Nemy Banthia, Moe Cheung, Marie-Anne Erki, Martin Faoro, Atsuhiko Machida, Aftab A. Mufti, Kenneth W. Neale, and Gamil Tadros, published in this journal (Vol. 4, No. 1, February 2000) is presented. The discussers would like to have the authors' views on two topics. The first is the statistical treatment of fiber-reinforced plastic structures. The second is the size effect in fiber-reinforced structures. Discussion is followed by closure from the authors.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".