Bibliographic record
Abstract
The network devices in the IEEE 802.3 based Ethernet networks use a carrier-sense-multiple-access with collision-detection (CSMA/CD) protocol to access the shared bus. There is no priority based access control to the shared bus in this protocol. All stations (nodes) run the same algorithm to access the bus and perform an exponential back-off when contention arises. This makes it difficult to achieve quality-of-service (QoS) guarantees (relative or quantitative) among different applications. Even if priority queuing is implemented, all the stations on the network use the same back-off algorithm at the medium access control (MAC) layer and thus does not provide any priority-based access to the bus. As there is a wide deployment of Ethernet networks, any modification to the protocol should inter-operate with existing devices, as it would be very difficult to upgrade all devices. This paper proposes a modification to the back-off scheme which does not require any modifications to the existing devices, but newer devices, such as voice-over-IP (VoIP) phones and multimedia devices can make use of the modification to get a relative priority over others. Both analysis and simulation results confirm that, using differentiated back-off, high-priority traffic get quicker access to the bus (higher throughput and lower delay) than low priority traffic
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".