Molecular Simulations and Biomembranes
Bibliographic record
Abstract
The need for information in the understanding of membrane systems has been caused by three things - an increase in computer power; methodological developments and the recent expansion in the number of researchers working on it worldwide. However, there has been no up-to-date book that covers the application of simulation methods to membrane systems directly and this book fills an important void in the market. It provides a much needed update on the current methods and applications as well as highlighting recent advances in the way computer simulation can be applied to the field of membranes and membrane proteins. The objectives are to show how simulation methods can provide an important contribution to the understanding of these systems. The scope of the book is such that it covers simulation of membranes and membrane proteins, but also covers the more recent methodological developments such as coarse-grained molecular dynamics and multiscale approaches in systems biology. Applications embrace a range of biological processes including ion channel and transport proteins. The book is wide ranging with broad coverage and a strong coupling to experimental results wherever possible, including colour illustrations to highlight particular aspects of molecular structure. With an internationally respected list of authors, its publication is timely and it will prove indispensable to a large scientific readership.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".