Cooperative data dissemination via roadside WLANs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data dissemination services embrace a wide variety of telematic applications where data packets are generated at a remote server in the Internet and destined to a group of nomadic users such as vehicle passengers and pedestrians. The quality of a data dissemination service is highly dependent on the availability of network infrastructures in terms of the access points. In this article, we investigate the utilization of roadside wireless local area networks (RS-WLANs) as a network infrastructure for data dissemination. A two-level cooperative data dissemination approach is presented. With the network-level cooperation, the resources in the RS-WLANs are used to facilitate the data dissemination services for the nomadic users. The packet-level cooperation is exploited to improve the packet transmission rate to a nomadic user. Various techniques for the two levels of cooperation are discussed. A case study is presented to evaluate the performance of the data dissemination approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it