Verification process and its application to network traffic simulation models
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
Abstract This paper summarizes a standardized verification process for network traffic simulation models. After the general introduction of philosophy of verification, we explain detailed processes of the verification and its application to several well‐known simulation models. “Verification” here means several examination tests of simulation models using virtual data on a simple network so as to confirm their fundamental functions. In the course of model development, the developers have to examine whether the model performance is consistent with the specifications that they intend and also with the well‐authorized traffic engineering theory. Because of several constraints in putting the model specifications into the computer programming such as discretizing of time and space and simplifying vehicle behaviors to some degree, the intended model specifications may not be fully achieved in a computer. Therefore, we strongly recommend the verification before applying the models to a real network.
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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 | 0.000 |
| 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) | 0.000 | 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