Bibliographic record
Abstract
For some years, we have taught an introductory course in biomechanics within the Department of Mechanical and Industrial Engineering at the University of Toronto. We have been unable to find a textbook suitable for the purpose of introducing engineers and others having a “hard science” background to the field of biomechanics. That is not to say that excellent books on biomechanics do not exist; in fact, there are many. However, they are typically at a level that is too advanced for an introductory course, or they cover too limited a subset of topics for purposes of an introductory course. This book represents an attempt to fill this void. It is not meant to be an extensive treatise on any particular branch of biomechanics, but rather to be an introduction to a wide selection of biomechanics-related topics. Our hope is that it will aid the student in his or her introduction to the fascinating world of bioengineering, and will lead some to pursue the topic in greater detail. In writing this book, we have assumed that the reader has a background in engineering and mathematics, which includes introductory courses in dynamics, statics, fluid mechanics, thermodynamics, and solid mechanics. No prior knowledge of biology, anatomy, or physiology is assumed, and in fact every section begins with a review of the relevant biological background. Each chapter then emphasizes identification and description of the essential aspects of the related biomechanics problems.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.547 | 0.345 |
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".