UBC-SMMB/ArtificialCatchBondMY2024: Data and Code, Zenodo Release
Bibliographic record
Abstract
Contains all data necessary to reproduce the figures in the paper, as well as two scripts which produce the figures and run the simulations. Also contains force-extension data from optical trap experiments and the simulated jaw/hook intersections. System Requirements OS: Windows 10+ Software: MATLAB 2023b+ Installation Download the contents of this zip file. Add the folder + subfolders to your working directory in MATLAB. Generating the figures used in the paper Clear your workspace. Import the contents of FigureData.mat. Run the ArtificialCatchBondFigures.m script. The subsections of this script should be runnable in any order, with the exception of the first section, which processes the data which was imported and thus must be run first. Running the Analytical Simulation Clear your workspace. Open runCatchBondSim.m. Set parameters in the first subsection: number of sequences to generate and average (nSeq) sequence (sLength array containing the DNA lengths you would like to probe; minimum length is 7 bp) salt (struct containing sodium, potassium, and magnesium concentrations in M) Run subsection 1. Depending on how many sequences/parameters you are testing, this could take several minutes. Run subsection 2. This will create an intersections array containing all sequence pairs which have the jaw and hook unzipping in the correct order. Running the Monte Carlo Simulation If you have just run the analytical simulation, skip to step 5. Clear your workspace. Import the contents of IntersectionsData.mat. Open runCatchBondSim.m. Scroll down to the heading "3. Monte Carlo Simulation to get catch bond rupture times" There are 3 modes: non-linear force ramp (this is what was used in the paper). It simulates our specific optical tweezers trap stiffness and our specific dsDNA handle length, which leads to non-linear force ramps. linear force ramp. force clamp. Set the initial parameters (commented) and run the section which corresponds to the mode you wish to use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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; both teacher heads agree on what is shown here.
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".