Bibliographic record
Abstract
This special issue contains selected papers that were presented at the ITCOM conference on Internet Performance and Control of Network Systems that was held in Boston, MA, July 30–31, 2002. The conference was part of the ITCOM symposium—organized by SPIE, the international Society for Optical Engineering-aiming to bring together researchers and practitioners from Information Technology and Communications. The purpose of the conference was to promote the discussion on the development of performance evaluation techniques, traffic control principles and traffic engineering methods and practices. The selected papers are all extended versions of papers presented at the conference and are included in Ref. [1]. The papers have been reviewed by the following subject-matter experts, whose efforts are reflected in the high quality of the papers presented here: K. Chandra (University of Massachusetts), J. Charzinski (Siemens, Germany), G.L. Choudhury (AT&T Labs), J. Klincewicz (AT&T Labs), R.E. Kooij (TNO Telecom, Netherlands), D. Liu (AT&T Labs), M.R.H. Mandjes (CWI and Twente University), P.K. Reeser (AT&T Labs), R. Rodriguez-Dagnino (ITESM, Mexico), O. Rose (University of Wuerzburg, Germany), H. Takagi (University of Tsukuba, Japan), C. Williamson (University of Calgary, Canada). The authors also wish to thank Jeremy Thompson, Editor-in-Chief, for his interest in the conference and his efforts in publishing this Special Issue.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".