Bibliographic record
Abstract
GPON and EPON, described at length above in Chapter 3 and Chapter 4, respectively, represent the two most important Gigabit-class passive optical networks (PONs) that are widely deployed in the United States, Europe, and Asia Pacific region. Given the ever increasing bandwidth demand from consumer and business applications, current PONs are expected to evolve into next-generation PONs (NG-PONs) over the next couple of years. GPON and EPON are expected to coexist for the foreseeable future as they evolve into NG-PONs. Clearly, one way to realize NG-PONs is to increase the line rate of current Gigabit-class PONs to 10 Gb/s. A good example of this approach is the IEEE 802.3av 10G-EPON standard, which was approved in September 2009 (see Section 4.4). NG-PONs are mainly envisioned to (i) achieve higher performance parameters, e.g., higher bandwidth per subscriber, increased splitting ratio, and extended maximum reach, than current GPON/EPON architectures, and (ii) broaden GPON/EPON functionalities to include, among others, the consolidation of optical access, metro, and backhaul networks, and the support of topologies other than conventional tree structures. Network operators are seeking NG-PON solutions that can transparently coexist with legacy PONs on the existing fiber infrastructure and enable gradual upgrades in order to avoid costly and time-consuming network modifications and stay flexible for further evolution paths.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".