MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
Bibliographic record
Abstract
Background Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. Contents There are three tarball (.tar.gz) files containing the core simulation data: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains: 1. a source PDB (.pdb) file 2. Five AMBER trajectory (.nc) files for five independent MD simulations, numbered 1 to 5. Note: each of these files is over 2GB. There is an additional tarball containing the control files and scripts used for running the MD simulations: 1. Multiple control (.ctl) files numbered 1 to 10 that are used to minimize (min prefix), relax (rel prefix) and equilibrate (equ prefix) the model 2. Executable do_md that performed all the minimisation, relaxation and equilibration steps 3. control file prod.ctl used for the production run 4. Executable run_prod that was used to perform the production run 5. Two control files (prod_short.ctl and prod_short_2.ctl) for the short runs used to de-correlate the simulation for the independent runs 6. Executable run_short and run_short_2 used to carry out the de-correlated production runs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".