Bibliographic record
Abstract
Local area networks based on passive optical star couplers were originally proposed to exploit the enormous bandwidth of optical fiber [1]. Unfortunately, the maximum throughput of these systems is restricted by the limited number of channels which can be achieved in practice. To address this problem, two‐level hierarchical WDM LANs have been proposed [2–4]. These architectures can increase system capacity by permitting a set of local channels (i.e., the local waveband) to be spatially reused across a number of local optical networks (LONs). Full connectivity is maintained by a globally shared remote waveband. In systems of this kind the channels are typically accessed using a single wavelength‐agile transmitter at each station [4]. Since fast receiver tuning is currently more difficult to achieve, practical designs are based on dynamic transmitter tuning only [2–4]. Receiver tuning is performed only rarely or during system initialization. An unfortunate complication with hierarchical networks is that stations must maintain a presence in both wavebands. In fixed‐tuned receiver designs this means that each station must have one receiver for each waveband. As a result, the amount of station receiver hardware is at least twice that required for single passive star networks just to maintain full connectivity. In this paper, a simple technique is proposed for eliminating this requirement. In the remote section of the network, fixed wavelength conversion is used to fold certain remote wavelengths onto the local ones. This results in an interesting design where the local/remote allocation of bandwidth is dynamically controlled temporally by the stations from the edge of the network. Full connectivity can be maintained with only half the total number of station receivers and the capacity can scale linearly with the number of LONs, as in conventional hierarchies. The price to pay for this simplification however, is that capacity is lost through the wavelength folding process. New media access protocols are required to prevent conflicts in this case. The capacity and delay performance of the system is investigated for four proposed media access control protocols.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".