A simplified optical star network using distributed channel controllers
Bibliographic record
Abstract
In the paper, a new network architecture is presented for single-hop passive optical star networks. The objective is to simplify the user stations as much as possible, thus giving an economical design. This is accomplished by using a set of distributed channel controllers, one for each WDM channel. The network will thus be referred to as DCCN (for distributed channel controller network). The channel controllers assist in the operation of the network in a number of ways. In the network, the allocation of bandwidth is hierarchical and is achieved independently on each channel. This simplification decouples system operation into two levels. At the higher level, bandwidth partitioning may be done in a static or dynamic fashion. The lower level determines the dynamic use of slots. Two options for media access are proposed. The first is a centralized approach based upon an "asynchronous request switch" design. The second is much more distributed. Each set of competing stations builds a distributed queue based upon observed requests. Capacity results are presented for the design and compared with other protocols. It is found that the proposed architecture has much higher capacity than in many other networks with similar hardware requirements.>
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".