Bibliographic record
Abstract
From quantum theory to statistical mechanics, the methodologies of physics are often used to explain some of life's most complex biological problems. Exploring this challenging yet fascinating area of study, Molecular and Cellular Biophysics covers both molecular and cellular structures as well as the biophysical processes that occur in these structures. Designed for advanced undergraduate and beginning graduate students in biophysics courses, this textbook features a quantitative approach that avoids being too abstract in its presentation.Logically organized from small-scale (molecular) to large-scale (cellular) systems, the text first defines life, discussing the scientific controversies between mechanists and vitalists, the characteristics of living things, and the evolution of life. It then delves into molecular structures, including nucleic acids, DNA, RNA, interatomic interactions, and hydrogen bonds. After looking at these smaller systems, the author probes the larger cellular structures. He examines the cytoplasm, the cytoskeleton, chromosomes, mitochondria, motor proteins, and more. The book concludes with discussions on biophysical processes, including oxidative phosphorylation, diffusion, bioenergetics, conformational transitions in proteins, vesicle transport, subcellular structure formation, and cell division.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.022 |
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".