Bibliographic record
Abstract
The gigabit passive optical network (GPON) is an outcome of the full service access network (FSAN) alliance and is specified in the ITU-T G.984.x series of recommendations, which were finalized in February 2004. GPON extends the capabilities of its two predecessors, asynchronous transfer mode (ATM) PON, also known as APON, and broadband PON (BPON). Compared with its predecessors, GPON provides larger splitting ratios, higher up- and downstream data rates, longer reach, improved privacy and security through the use of the Advanced Encryption Standard (AES) algorithm, and a new GPON encapsulation method (GEM) to carry synchronous voice services and data services such as Ethernet in a bandwidth-efficient manner (Shumate [2008]). These extended capabilities of GPON are explained in greater detail in the following. Architecture Figure 3.1 shows the architecture of a GPON network (Effenberger et al . [2007]). GPON deploys two different wavelength channels for upstream and downstream communication. The upstream and downstream wavelength channels operate at 1310 nm and 1490 nm, respectively. Several upstream and downstream data rates are specified for GPON, with a maximum data rate of 1.244 Gb/s in the upstream direction and 2.488 Gb/s in the downstream direction. The reach of a GPON network can be as high as 60 km, whereby the differential reach between optical network units (ONUs) must not exceed 20 km. The ITU-T recommendations for GPON allow for a splitting ratio of up to 128.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.176 | 0.108 |
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".