Bibliographic record
Abstract
On behalf of the organizing committee, it is our great pleasure to welcome you to the 2007 Summer Computer Simulation Conference (SCSC'07) and to the city of San Diego. SCSC'07 this year is a collection of 17 unique tracks ranging from a DEVS Workshop to Methodology, Tools and Software Applications (MTSA). The conference offers a unique forum for researchers and modeling and simulation professionals to share their achievements; and it provides an opportunity to explore the depth and breadth of many techniques, methodologies, tools, and their applications. The conference committee has developed a program that includes keynote speeches, plenary and panel sessions, a tutorial, featured speakers, and opportunities for informal gatherings in addition to the technical sessions. In response to the SCSC'07 Call for Papers, the conference committee received 229 technical articles. The articles were peer reviewed by two or more experts in the area. The accepted papers were categorized as Invited (IN), Full (FP), Short (SH), Student (ST), and Poster (PO), and grouped them into 46 technical sessions, as indicated in the program announcement. The credit for the success of this conference goes to the track organizers, international technical program committee members, and reviewers. It is they who worked diligently, enlisted participants, reviewed papers, organized plenary and panels sessions, and finalized the program. SCSC'07 includes several new tracks, including Computational M&S of Embedded Systems, 3D visualization, Environmental M&S, Crowd M&S and the Model-Based Specification and Simulation-Based Design and Procurement, exceptionally organized by Mr. Terry Ericsen from ONR.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.029 |
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".