Layer 1 virtual private network management by users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The layer 1 virtual private network (LlVPN) technology supports multiple user networks over a common carrier transport network. Emerging L1VPN services allow: L1VPNs to be built over multiple carrier networks; L1VPNs to lease or trade resources with each other; and users to reconfigure an L1VPN topology, and add or remove bandwidth. The trend is to offer increased flexibility and provide management functions as close to users as possible, while maintaining proper resource access right control. In this article two aspects of the L1VPN service and management architectures are discussed: management of carrier network partitions for L1VPNs, and L1VPN management by users. We present the carrier network partitioning at the network element (NE) and L1VPN levels. As an example, a transaction language one (TL1) proxy is developed to achieve carrier network partitioning at the NE level. The TL1 proxy is implemented without any modifications to the existing NE management system. On top of the TL1 proxy, a Web services (WS)-based L1VPN management tool is implemented. Carriers use the tool to partition resources at the L1VPN level by assigning resources, together with the WS-based management services for the resources, to L1VPNs. L1VPN administrators use the tool to receive resource partitions from multiple carriers and partner L1VPNs. Further resource partitioning or regrouping can be conducted on the received resources, and leasing or trading resources with partner LlVPNs is supported. These services offer a potential business model for a physical network broker. After the L1VPN administrators compose the use scenarios of resources, and make the use scenarios available to the L1VPN end users as WS, the end users reconfigure the L1VPN without intervention from the administrator. The tool accomplishes LlVPN management by users
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it