Bibliographic record
Abstract
Networks and their services have been growing rapidly in recent years, so has the complexity of configuring the network operations. In order to manage the rapidly expanding networks, new management protocols are thus needed to change the network management from configuration of individual devices to automation of the whole network. SNMPCONF, a policy-based configuration with SNMP protocol, is proposed by IETF to meet this requirement. SNMPCONF is suggested to configure networks with a policy MIB (Management Information Base). However, deploying this protocol in existing network devices needs more work, because the policy MIB defined by SNMPCONF is not implemented now in the present network and no configuration MIB defined. Even if the configuration MIB and policy MIB implemented by vendors in future, some existing devices may not be upgraded to support these MIB for their hardware limitation (e.g., memory limit). Another problem is found in the communication between a SNMP supported network and the SNMP unsupported networks (e.g. telecommunication network where TL1 is used in North America). In this study, CLI and TL1 are used to help solving these problems. Based on Modular SNMP from University of Quebec at Montreal, a JAVA framework to deploy SNMPCONF with this approach is implemented and tested on a network built with Cisco routers (Cisco IOS 12.0, routers ranged from Catalyst 2600 to 2900). The preliminary work shows the SNMPCONF can be implemented by wrapping CLI commands as accessory functions in policy language without configuration MIB. As shown in this work, another advantage of this approach is that policy execution is atomic and persistent.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".