Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
Bibliographic record
Abstract
On behalf of the Organizing Committee, it is our great pleasure to welcome you to the 2013 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS). Building upon 26 years of history and the reputation for high quality papers of the PADS Workshop (Workshop on Principles of Advanced and Distributed Simulation), the new SIGSIM-PADS focuses on the intersection of computer science and modeling and simulation. This year's Conference continues its tradition of being the premier forum for presentation of research results in this field of research, including high-quality papers in all aspects of simulation technology. The program includes a wide selection of technical presentations, plenary and invited speakers, and a collection of state-of-the-art presentations and articles related to research, development, and applications of Theory of Modeling and Simulation. The Call for Papers attracted 75 Full Paper submissions from Algeria, Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, France, Germany, Iran, Israel, Italy, Korea, Netherlands, Norway, Poland, Portugal, Serbia, Singapore, Spain, Sweden, Turkey, United Arab Emirates, United Kingdom and United States, making it a truly International event. The program committee accepted 29 of those Full Papers. Also, 11 Work-in-Progress papers and 3 Invited Papers are included in the program. This year's sessions include new topics in the field: Automation in Generation of Simulation Models, Heterogeneous Parallel Simulation, Theory and Emergent Behavior, Parallel Simulation in Multicore Architectures, Management of Activity in Simulation and a Panel on Grand Challenges in Modeling and Simulation. Also, advanced papers on Parallel Simulation algorithms, Agent-Based Simulation, Distributed Simulation, Simulation with Hardware-in-the-loop and varied Applications including Networking and Communications, Logistics, and Biology. This year we have organized the First ACM SIGSIM-PADS Ph.D. Colloquium and Poster Session. During this session, students will have the opportunity to discuss their research with top experts in the field. A Best Colloquium Award will be presented to the best presentation. The Awards Committee chose the top-five Conference Best Papers, and, based on the reviewer's evaluations and the Committee evaluations, the Best Paper Award has been selected, which will be announced during the Conference and posted in the Conference webpage after the event.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".