Bibliographic record
Abstract
In this paper, we shall discuss a novel design on the policy-based management for the Internet. This design deploys the concept of active networking. As opposed to the traditional network design, active network empowers network node with the ability to manipulate data and program code in packets, and configure the network properties according to the needs of different applications. The policy-based management can control network routers in order to realize end-to-end Quality of Service (QoS), such as differentiated and integrated services, across the Internet. For the moment, the Internet Engineering Task Force (IETF) has defined the framework of the policy-based management. It employs a simple client/server model that uses Common Open Policy Service (COPS) protocol to facilitate policy management and control. Our design of Active Bandwidth Broker (ABB) belongs to an active application. Our goals are to distribute centralized workload of the policy-based management over multiple active nodes in the active networks, introduce mobility of the bandwidth brokers, and allows load sharing to the policy-based management. This results a network-wide intelligent, highly available, and consistent QoS control that allows performance protection for voice, video and Internet business application while reducing costs for growing networks.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".