Investigation of Versatile Datatypes for Representing Time in Discrete Event Simulation
Bibliographic record
Abstract
Discrete-Event Simulation (DES) is a technique in which the simulation engine plays a history following a chronology of events in which the processing of each event takes place at discrete points of a continuous timeline. The simulator must interact actively with time variables for reproducing the chronology of events over positive real numbers, which is usually represented by approximated datatypes as floating-point. Nevertheless, the approximation made by commonly used datatypes in simulations can affect the timeline, preventing the generation of correct results. To overcome this problem, we present two new versatile datatypes to represent time variables in DES. These new datatypes provide a wider range of numbers reducing approximation errors, and if an error occurs, the simulation user is notified. To test our datatypes, we perform an empirical evaluation in order to compare their performance.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".