A Cell-DEVS Visualization and Analysis Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although Cell-DEVS engines have been optimized for efficient modelling and simulation over a cell space, visualization and analysis of the results they generate remains a complex task. They are limited by the high volume of data that must be processed to identify patterns and tendencies in the model's behavior, particularly over time. In this paper, we present a lightweight, web-based visualization and analysis platform as an alternative to costly proprietary software. The Cell-DEVS Simulation WebViewer is written in HTML5 and JavaScript, requires no installation and offers a user-friendly way to post-process Cell-DEVS simulation results. It allows users to visualize and animate their simulation results, to navigate to different time steps, record videos of their simulation, inspect the state of individual cells, and export the raw data in JSON for further processing in other external programs. It leverages the data-driven document (D3) JavaScript API to provide statistical analysis capabilities in the form of animated charts that display data derived from the simulation as it is being executed.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it