Bibliographic record
Abstract
A network management environment with dynamic behaviour can be used as a base for developing a wide range of applications from active networks to "on the fly" customised network management interfaces. This paper presents the dynamic features of the "BaseLayer", a network management tool developed by Machine Intelligence Research Laboratory, University of Ottawa. The dynamic features include the possibilities of adding new managed object (MO) classes without turning down the network management application and of adding new methods and attributes to MOs without turning down the factory servers for those managed objects. The second facility defines the Dynamic Managed Objects (DMOs) that can be changed at runtime by adding new methods and attributes "on the fly". While for adding new managed object classes the Interface Repository (IFR) and the Dynamic Invocation Interface (DII) provided by CORBA were used for DMO implementation a master-shadow server architecture was designed. In the new architecture each DMO factory server has at least 2 processes: a master process, which holds the MO instances and their attributes, and one or more shadow processes, which hold the new added methods.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".