SELFCON: An architecture for self-configuration of networks
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
Traditional configuration management involves complex labor-intensive processes performed by experts. The configuration tasks such as installing or reconfiguring a system, provisioning network services and allocating resources typically involve a large number of activities involving multiple network elements. The network elements may be associated with proprietary configuration management instrumentation and may also be spread across heterogeneous network domains thereby increasing the complexity of configuration management. This paper introduces an architecture for the self-configuration of networks (SELFCON). The proposed architecture involves a directory server, which is used to maintain configuration information. The configuration information stored in the directory server is modeled using the standard DEN specification thereby allowing effective exchange of network, system and configuration management data among heterogeneous management domains. SELFCON associates configuration intelligence with the components of the network, rather than limit it to a centralized management station. The network elements are notified about related changes in configuration policies, based upon which, they perform self-configuration. SELFCON is able to provide automation of configuration management and also an effective unifying framework for enterprise management.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it