STARS: A Framework for Statistically Rigorous Simulation-Based Network Research
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
Simulation has become one of the dominant tools in wired and wireless network research. With the advent of cloud, grid, and cluster computing it has become feasible to use parallelization to perform richer larger-scale simulations. Moreover, the computing resources needed to perform statistically rigorous simulations are now easily obtainable. Although a number of parallel network simulation frameworks exists, the issue of statistical rigorous testing has largely not been addressed. This work presents a parallel MPI-aware network simulation framework that is specifically designed to provide automated support for statistically rigorous experimentation, thereby offloading this significant researcher burden. Unlike prior frameworks, the proposed framework includes a distribution-free statistical analysis feedback loop that automatically deduces the next set of experiments that need to be run. The value of this new framework is highlighted by exploring the well known issue of assessing the true duration of start-up transients within mobile ad hoc networks (MANETs) simulations.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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