An Advanced Data Type with Irrational Numbers to Implement Time in DEVS Simulator
Bibliographic record
Abstract
In the Discrete-Event System Specification (DEVS) time variables are Real numbers. This is common to other simulation formalisms for Discrete-Event Simulation (DES). Current simulators for these formalisms approximate time variables using floating-point or rational representations. Neither of them is capable to adequately represent irrational numbers. The representation of these numbers is important, especially for studying systems with geometrical properties. The use of approximations, as floating-point, may silently introduce errors to the causality chain. These errors may produce incorrect simulation trajectories without informing about them. Here, we propose a new data type combining rational data types with computable calculus concepts. This new data type extension provides representation, and operation, with subsets of irrational numbers. The proposed data type only provides four operations (+, -, <, =), those are sufficient for implementing simulators for DEVS and other DES formalisms. Usage of this data type has no significant complexity penalty for simulation not using irrational numbers.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".