Bibliographic record
Abstract
This book is based on notes prepared for a senior undergraduate or beginning graduate course that we taught at the universities of Alberta and Victoria. It is primarily intended for use by students of mechanical and civil engineering, but it may be of interest to others. The mathematical background required for the topics covered in the book is modest and should be familiar to senior undergraduate engineering students. In particular it is assumed that a reader has a good knowledge of classical vector mechanics and linear algebra. Also, a background of the classical thermodynamics usually taught in undergraduate engineering courses is desirable. One motivation for the book is to present an introduction to continuum mechanics that requires no background in certain areas of advanced mathematics such as functional analysis and general tensor analysis. The treatment of continuum mechanics is based on Cartesian tensor analysis, but orthogonal curvilinear coordinates and corresponding physical coordinates are considered in appendices. A list of books that consider tensor analysis and applications is given at the end of chapter 1. Several of the books are out of print but may be useful to students if they can be obtained from libraries. Mathematica is used for symbolic manipulation, numerical computation, and graphs where appropriate, and its use is encouraged. Chapter 1 is a detailed introduction to Cartesian tensor analysis. It differs from some other treatments of the topic, for example, the early texts by Jeffreys and Temple, by emphasizing both symbolic and suffix notation for first-(vectors) and second-order tensors.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.447 | 0.333 |
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".