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
Vehicular safety applications are based on broadcasting of safety messages to the neighboring vehicles. As LTE is an infrastructure-based network, vehicles cannot broadcast their safety messages directly to their neighbors; thus, all messages should pass through the infrastructure. However, these messages may congest the network and lead to high delays for some vehicles. Furthermore, in vehicular safety applications, faster vehicles are more sensitive to delay, since their positions change more frequently than slower vehicles within the same time frame. Therefore faster vehicles should be assigned a higher priority to send messages in order to have low delay. Existing LTE schedulers do not consider this factor when allocating resources. This leads to high delays and unacceptable position errors for faster vehicles. In this paper we propose a Speed and Location Aware (SLA) scheduler for LTE which is suitable for vehicular applications. SLA scheduler considers the speed and location of vehicles in order to assign priorities for resource allocation. Faster vehicles receive priority for the allocation of Physical Resource Blocks (PRB). Simulation results show that SLA scheduler outperforms existing algorithms by preventing high delays and large position errors for faster vehicles.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it