Bibliographic record
Abstract
The number of services that will become available in distributed networks (in particular, on the Internet) is expected to grow enormously. Besides classical services such as those offered by printers, scanners, fax machines, and so on, more and more services will be available nowadays. Examples are information access via the Internet, music on demand, Web services, and services that use computational infrastructure that has been deployed within the network. Moreover, the concept of service in mobile agent systems, which will be described in this article, has come into prominence recently. The mobile agent model is a new distributed software development paradigm as compared to the traditional client-server model. Instead of calling operations on servers with some form of synchronization, the user passes on his or her goal to an agent that can migrate within the computational environment and knows how to handle it without being controlled. In brief, mobile agents are active, autonomous, intelligent objects that are able to move between locations in a so-called agent system. Mobile agents must interact with their hosts in order to use their services or to negotiate services with other agents (Song & Li, 2004). Discovering services for mobile agents comes from two considerations. First, the agents possess local knowledge of the network and have a limited functionality, since only agents of limited size and complexity can migrate efficiently in a network and have little overhead. Hence, specific services are required that aim at deploying mobile agents efficiently in the system and the network.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".