Dataset for: Revealing pH-dependent antimicrobial peptide, GL13K, characteristics: A constant pH molecular dynamics study
Bibliographic record
Abstract
In version 2 of data, ipython notebook (final_MSM_tica.ipynb) for MSM and FES of the individual system is provided. In version 1, these datasets contain molecular dynamics trajectories and structural files for each system (10 systems * 5 replicas each) [XTC and PDB file] Lambda trajectory for each system (10 systems * 5 replicas each * 4 lysines) Sample Gromacs MDP files ForceField sample builder log file Bash codes for some level of post-simulation analysis/file extraction Analysis codes The simulations were done in explicit water, but the water has been removed to make the files a reasonable size for upload. The pre-registered plan describing this project is available on Open Science Foundation with the following DOI: https://doi.org/10.17605/OSF.IO/EW4VM Acknowledgements: This research was supported in part by Discovery Grant #RGPIN-2021-03470 from the National Sciences and Engineering Research Council of Canada. This research was enabled in part by support provided by Calcul Quebec (www.calculquebec.ca) and the Digital Research Alliance of Canada (https://alliancecan.ca). This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program under grant number CRC-2020-00225.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.068 |
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".