Bibliographic record
Abstract
To the student I wrote this book for you. When I came to the University of Lethbridge in 1995, I started teaching physical chemistry to a mixed class of chemistry and biochemistry students. I have been teaching versions of this course ever since. My first year here, I picked a book that I liked. Boy, was that a mistake! First of all, the book contained almost no examples that appealed to the biochemists, who were the majority of the students in the class. Second, it was filled with mathematical derivations, which I found very satisfying, but which sometimes obscured the concepts of physical chemistry for the students. Having made this mistake, I started looking around for other textbooks. The ones that I liked the best, Barrow's Physical Chemistry for the Life Sciences and Morris's A Biologist's Physical Chemistry , were out of print at the time. We used a few different textbooks over the years, and some of them were very good books, but my students were never completely happy, and so I wasn't happy. In some cases, the books contained too much math and not enough insight. In others, too many equations were presented without derivation or explanation, which undermined the students' understanding of the material. I therefore set about writing a book for my students, and therefore for the broader community of life science students who need a term of physical chemistry.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.453 | 0.269 |
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".