Bibliographic record
Abstract
Electronic commerce technology is more and more present and people are getting connected together. Companies and users want to automate their task and discover new business opportunities. Many standards and initiatives have been already proposed. Most of them focus on business-to- business protocol definition. Rosettanet, cxml, xcbl and bizTalk are only a few examples. Users and companies then have a set of protocols they need to understand and master for implementing solutions. With the increasing number of protocols, companies need a solution permitting them to easily integrate different protocols. Unfortunately, the commercial solutions provided today do not provide sufficient support for integration. Software agents have already been proposed as a key technology for connecting people and adding new features in electronic trading. However, most of the agent-based solutions already proposed have not really changed the user's experience, neither have they brought a major shift from traditional activities in classic markets. This is mainly due to their inflexibility. In fact, these solutions predefine rules and policies in ways that users feel uncomfortable and are limited in their actions. Agent-based systems seem to appear as a key technology that gives users more autonomy and that is yet flexible enough to integrate new business scenarios. In this paper we propose an agent-based solution that could be used to wrap in existing systems and applications. We show how it could be easy to support new protocols without being forced to rewrite solutions from scratch. The work proposed combines both, advantages from software agent technology, and from P2P networking technology. The solution has four layers where each one encapsulates a specific set of functionalities: communication, business, cooperation and coordination. Users can adapt or modify the internal structure of one layer without necessarily affecting the others.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".