Bibliographic record
Abstract
The rapid increase in the number of wavelengths per fiber has significantly increased the size of the optical crossconnect (OXC) in WDM transport networks. To maintain the scalability of the OXC at a reasonable level, various multi-granular OXC (MG-OXC) architectures have been proposed. Motivated by the fact that the MG-OXC constitutes only the optical segment of the generalized multi-protocol label switching (GMPLS)-based transport node architecture, we first propose a novel node architecture that handles the whole traffic hierarchy defined in GMPLS. Second, incorporating the proposed architecture with our novel contributions, namely (1) bifurcation of multi-granularity traffic demands; (2) traffic flow grouping strategies at all granularity levels, requires defining a new transport planning problem, which we call the routing and multi-granular paths assignment (RMGPA) problem. The RMGPA problem is formulated as a mixed integer linear programming (MILP) model with the objective of minimizing the overall network weighted port count
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".