Bibliographic record
Abstract
This chapter describes the Next Generation MVPN (NG MVPN) approach to multicast VPN in SR OS (Alcatel-Lucent Service Router Operating System). NG MVPN is a standardized and more generalized approach to MVPN that effectively supersedes Draft Rosen. NG MVPN uses BGP Auto-Discovery (A-D) to discover MVPN members, and either PIM/GRE or MPLS point-to-multipoint (P2MP) LSPs to transport multicast data. The tunnel identifier field varies depending on the tunnel type. The tunnel identifier field is described in more detail later. Although PIM is still an option for building the I-PMSI and S-PMSI MDTs in an NG MVPN, MP-BGP is the primary protocol used for signaling. NG MVPN uses MP-BGP to discover the member PEs. Until now, the tree used to transport customer data across the core has been a PIM MDT. With the use of BGP for handling customer PIM signaling, PIM is not required in the service provider core.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.062 |
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".