Caching and forwarding assistance for vehicular information services with mobile requesters
Bibliographic record
Abstract
Smart vehicles have been capturing increasing attention as major providers of ubiquitous information services. In this paper, we propose a solution to enable expedited and cost-effective access of road information for vehicles as information requesters. The proposed caching-assisted data delivery with mobile requesters (CADD-MR) scheme employs caching on the data delivery path for handling later interests in similar data. CADD-MR depends on the use of light-weight road caching spots (RCSs) deployed at intersections for caching and heading-aware forwarding, and on vehicles as data carriers. To support vehicles as mobile information requesters, CADD-MR makes use of the RCSs for keeping track of the mobility registry of the requesting vehicles to ensure that data replies reach their corresponding mobile destinations, as long as they are still not expired. Performance evaluation of CADD-MR demonstrates significant improvements in the access cost, delay, and delivery ratio compared to a scheme that does not use RCSs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".