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Record W2075350940 · doi:10.1109/itcc.2005.101

Client software agents in FCVW

2005· article· en· W2075350940 on OpenAlexafffund
Elhadi Shakshuki, Ivan Tomek, O. Prabhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsAcadia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceWorkspaceSoftwarePlug-inHuman–computer interactionSoftware agentVirtual machineSoftware engineeringDatabaseOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

Software agents are a rapidly developing field of computer science. The need for software agents is felt in various applications and environments. It has therefore become essential to study agents with respect to the various environments in which they operate. MITR's CVW (collaborative virtual workspace) is a collaborative virtual environment where agents can be created and perform important tasks for the user. CVW is very suitable for programming agents especially as they interact with objects, users, and virtual locations. This paper focuses on three types of agents programmed in FCVW (federated CVW), our extension of CVW. These agents include observer, retriever, and garbage-collector agents. The observer's functions are to observe the behaviour of users, objects, and files. The retriever agent helps a user to find other users, objects and rooms within FCVW. The garbage-collector agent monitors files to inform the user about files on the basis of predefined dates.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0060.007
Open science0.0040.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.004

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.

Opus teacher head0.041
GPT teacher head0.286
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2005
Admission routes2
Has abstractyes

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