Communication systems : the state of the art : IFIP 17th World Computer Congress - TC6 stream on communication systems : the state of the art, August 25-30, 2002, Montréal, Québec, Canada
Bibliographic record
Abstract
Preface. Research Advances in Middleware for Distributed Systems: State of the Art R.E. Schantz, D.C. Schmidt. Internet Routing: The State of the Art K. Ramasamy, A. Arokiasamy, P.A. Balakrishnan. Performance of Telecommunication Systems: State of the Art K. Kontovasilis, S. Wittevrongel, H. Bruneel, B. Van Houdt, C. Blondia. Enhancing Web Performance A. Iyengar, E. Nahum, A. Shaikh, R. Tewari. Network Management: State of the Art R. Boutaba, Jin Xiao. State of the Art of Service Creation Technologies in IP and Mobile Environments J. Jormakka, H. Jormakka. QoS, Security, and Mobility Management for Fixed and Wireless Networks under Policy-based Techniques G. Pujolle, H. Chaouchi. A Multicast Routing Protocol with Multiple QoS Constraints Li Layuan, Li Chunlin. Anonymous Internet Communication Based on IPSec Ronggong Song, L. Korba. Internet Interconnection Economic Model and its Analysis: Peering and Settlement M.B. Weiss, Seung Jae Shin. Dimensioning Company Intranets for IT Bandwidth S. Vincze.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.177 | 0.080 |
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".