Département d’Informatique et de Recherche Opérationnelle
Bibliographic record
Abstract
SSJ stands for stochastic simulation in Java. The SSJ library provides facilities for generating uniform and nonuniform random variates, computing different measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte Carlo methods, collecting statistics, and programming discrete-event simulations with both events and processes. This document provides a very brief overview of this library and presents several examples of small simulation programs in Java, based on this library. The examples are commented in detail. They can serve as a good starting point for learning how to use SSJ. The first part of the guide gives very simple examples that do not need event or process scheduling. The second part contains examples of discrete-event simulation programs implemented with an event view, while the third part gives examples of implementations based on the process view. 1SSJ was designed and implemented in the Simulation laboratory of the Département d’Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal, under the supervision of Pierre L’Ecuyer, with the contribution of Mathieu Bague, Sylvain
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.043 |
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".