Enhanced EPON auto-discovery for fast network and service recovery
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
The Ethernet passive optical network (EPON) is an emerging broadband access networking technology that offers flexible bandwidth with simplicity, reliability and cost effectiveness. The automated discovery of optical network units (ONU) and the coordination of the operation between ONU and the optical line termination (OLT) is the mandate of the multi-point control protocol (MPCP). The auto-discovery mechanism is for finding newly attached ONU as well as reconnecting active (or recovered) ONU in case of service interruptions. On the basis of the IEEE 802.3ah MPCP, this paper introduces a set of enhancements to the auto-discovery mechanism. With these improvements, the EPON MPCP is able to support carrier-grade telecommunications services by recovering quickly from failures with minimum service impact. Moreover, it leads to a scalable and efficient scheme for balancing system response and bandwidth efficiency.
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