An object-oriented analysis technique for developing object-oriented simulations in Silk.
Bibliographic record
Abstract
Simulation is very popular as an analysis tool for complex systems. A new paradigm shift towards object oriented simulations is hoping to make simulations even more powerful and accessible. However, in order for those not familiar with object oriented techniques to join this shift, methods must be developed for them to build object oriented simulations, until they acquire an understanding of this programming paradigm. This is an attempt to develop a procedure for just such novice model builders. This thesis begins with some background on object oriented paradigm concepts. It then provides an overview of several different object oriented analysis techniques and attempts to define the common steps of these approaches. Several different commercial object-oriented simulation software are described, with an in depth description of the software selected, Silk, a java based simulation software. The general object oriented analysis approach is then adapted for simulation purposes, resulting in a proposed object oriented analysis technique for developing simulations, and an example is given. Object oriented analysis techniques such as use-case modeling and noun extraction are used to identify potential objects in a discrete manufacturing system to be simulated. These objects are then mapped to existing object classes in the Silk software for final model building.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .Z36. Source: Masters Abstracts International, Volume: 42-03, page: 1031. Adviser: R. S. Lashkari. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".