A Multi-Agent System for Personal Messaging
Bibliographic record
Abstract
this report, provided that the source of such material is fully acknowledged. *Poster presentation at the Fourth International Conference on Autonomous Agents (Agents' 2000). NRC 43662. A Multi-Agent System for Personal Messaging John F. Meech, Katherine Baker, Edith Law, Ramiro Liscano Network Computing Group, Institute for Information Technology, National Research Council of Canada. M50, Montreal Road, Ottawa, Ontario, Canada K1A OR6 {John.Meech, Katherine.Baker, Edith.Law, Ramiro.Liscano}@iit.nrc.ca Keywords Intelligent Interface Agent, Intelligent Interface, Adaptive Interface, Seamless Messaging, Unified Messaging, Software Agent. Telecommunications. 1. INTRODUCTION There are now many ways of sending messages (such as faxes, telephone calls, pages, emails, etc.) and a corresponding multitude of ways of responding to them. Part of this variety arises from the separate systems that
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".