EPON: An extensive review for up-to-date dynamic bandwidth allocation schemes
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
Ethernet passive optical network (EPON) has been widely studied in literatures in the past few years. Researchers from all around the world are investigating EPON main challenges and dynamic bandwidth allocation (DBA) problems. This paper, reviews the most recent studies conducted in EPON networks and presents the new proposed DBA schemes. The reviewed schemes are classified according to the main challenge addressed by the investigator. A brief outline is given for each one along with a discussion of its performance and possible contribution to enhance EPON efficiency. Generally, the main purpose of this article is to review EPON problems and presents the up-to-date suggested solutions. Also to indicate that further studies need to be carried out if a single scheme that incorporates excellent bandwidth utilization with effective QoS support and guaranteed fairness is required.
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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 | 0.000 | 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