ronments Using Bit Torrent and Bargaining is hereby approved.
Bibliographic record
Abstract
me detailed reviews, guidance, and direction throughout my research. I would also like to thank Dr. Dusit Tao (Nangyang Technological University, Singapore) and Dr. Ekram Hossain (University of Manitoba, Canada) for their helpful suggestions and ideas for pursuing my research. I am also very grateful to my other committee members, Dr. John Chiasson, Dr. R. Jacob Baker and Dr. Thad Welch for their help and support. I also express my sincere appreciation towards the Department of Electrical and Computer Engineering at Boise State University for providing me with support. vi Wireless Access in Vehicular Environment (WAVE) technology has emerged as a state-of-the-art solution to vehicular communications. The major challenges in WAVE arise due to the fast changing communication environment and short durations of communications due to the high mobility and speed of vehicles. As a result, it is difficult to broadcast a large amount of data in such a network for Vehicle-to-Roadside
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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.306 | 0.146 |
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".