Bibliographic record
Abstract
Reviewer Performance Award: In recognition of the significant amount of review work done and as a statement of the importance of the quality of the review process (quality and depth of review, insightful and supportive feedback to authors, quality, timeliness and quantity of confidential comments to TPC co-chairs), NetSoft 2021 launched the Reviewer Performance Awards. The awards are presented to the following individuals. Thomas Zinner (NTNU, Norway), Alexander Clemm (Futurewei Technologies, USA), Holger Karl (Paderborn University, Germany), Jason J. Quinlan (University College Cork, Ireland), Miguel Neves (Dalhousie University, Canada). The Best Demo Paper Award is presented to: "Open and Programmable 5G Network-in-a-Box: Technology Demonstration and Evaluation Results" by Adnan Aijaz (Toshiba Research Europe Ltd, UK), Ben Holden (Toshiba Research Europe Ltd, UK), and Fanyu Meng (Toshiba, UK). The Best Student Paper Award is presented to "SoftTap: A Software-Defined TAP via Switch-Based Traffic Mirroring" by Sogand Sadrhaghighi (University of Calgary, Canada), Mahdi Dolati (University of Tehran, Iran), Majid Ghaderi (University of Calgary, Canada), and Ahmad Khonsari (University of Tehran, Iran). The Best Paper Award is presented to: "Physical Wireless Resource Virtualization for Software-Defined Whole-Stack Slicing" by Matthias Sander-Frigau (Iowa State University, USA), Tianyi Zhang (Iowa State University, USA), Hongwei Zhang (Iowa State University, USA), Ahmed E. Kamal (Iowa State University, USA), and Arun Somani (Iowa State University, USA).
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 | 0.027 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.355 | 0.336 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".