Proceedings of the 4th International Conference on Queueing Theory and Network Applications
Bibliographic record
Abstract
Welcome to QTNA2009! When invited to co-chair the Technical Program Committee, our first task was to draft a Call-for-Papers. We felt that, for QTNA2009 to stand out from the plethora of conferences and achieve impact, there must be a clear focus. The Call therefore emphasized the interaction between queueing theory (and related techniques) and telecommunication/computer networks. We received 46 abstract submissions, with authors from Algeria, Belgium, Canada, China, Finland, Hungary, India, Indonesia, Japan, Republic of Korea, Taiwan, Thailand and UK. The number was adversely affected by the 2008/09 international financial crisis, the following recession and resulting budget cuts. The geographical diversity, however, matched the QTNA transition this year from an Asia-Pacific Symposium to an International Conference. There were some submissions that were purely on queueing theory or classical applications (e.g. production systems), but most were about various aspects of networking (wireless sensors, protocol analysis, quality of service, etc.). Most papers had three reviews each, but some had more. Many submissions received good scores, so we set a high bar and chose 15 full papers that fit the Call to focus on the interaction between queueing and networking. We also selected a few papers for short presentation at the conference.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.086 | 0.030 |
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".