Bibliographic record
Abstract
Over the last few years, voice over Internet protocol (VoIP) evolved from enabling voice communications between computer terminals to providing much wider functionality of the public switched telephony network (PSTN). The idea of deploying VoIP in private branch exchanges (PBXs) is gaining credence among service providers, and this paper addresses some of the design issues related to an IP PBX implementation. Specifically, the paper explores the interoperability between Cisco call manager (CCM) and Linux-based asterisk PBXs corresponding to the enterprise-class and open source IP PBXs, respectively. The architecture of the networking testbed is documented for running various VoIP protocols between multi-vendor devices and IP PBXs. The results presented can be broadly defined into three areas of investigation: (i) the use of session initiation protocol (SIP) and inter asterisk exchange (IAX) trunks to integrate CCM to Asterisk PBXs and Asterisk to Asterisk PBXs, respectively; (ii) examining interoperability between the CCM and Asterisk PBX with advanced features like call display, voicemail, call detail records (CDR) and voicemail to e-mail transfer; and (iii) testing the resilience of voice quality against packet loss and delay in the open source PBX trunks with National Institute of Standards and Technology (NIST) network simulator.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".