Bibliographic record
Abstract
Dr. Taheri teaches courses in mechanics of materials, computations, finite element method (linear and non-linear), fracture mechanics and fatigue, and fiber-reinforced plastics. Dr. Taheri is mainly interested in understanding and modelling of structural and materials response with computational and experimental methods to offer cost-effective solutions. He has expertise in developing effective design and experimental programs for specific case studies. He has long record of experience in computational mechanics and experimental characterization (static & dynamic) of fiber-reinforced composite materials; structural rehabilitation and vibration-based damage detection of structures using smart sensors and materials; fatigue, and fracture of materials; characterization of impact response and other highly nonlinear events of structural materials. Dr. Taheri worked in industry for eight years before joining the Civil Engineering department in 1994. He has also been engaged in several consulting projects commissioned by prestigious entities such as the Canadian Space Agency, and the Defence Research Laboratories, as well as other industries. He is a registered Professional Engineer with the province of Nova Scotia. He is also a member of ASME and AAM.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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; both teacher heads agree on what is shown here.
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".