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Record W2166198950 · doi:10.1109/apscc.2008.255

Modelling the Sharing of Resources across Collaborative Sessions

2008· article· en· W2166198950 on OpenAlexaff
Bruce Spencer, Sandy Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComputer scienceSession (web analytics)Variety (cybernetics)Task (project management)World Wide WebResource (disambiguation)VisualizationService (business)MultimediaComputer network

Abstract

fetched live from OpenAlex

Service-oriented architectures can be used to provide multiple simultaneous sessions to users that wish to communicate over a variety of media. This gives rise to rich, highly effective communication sessions that can greatly enhance userspsila interaction. For example, a health services virtual organization seeks to use such tools for a variety of purposes: virtual patient simulation, anatomical visualization and virtually sharing cadaveric dissections. We propose SAVOIR, Service-oriented Architecture for Virtual Or- ganization Resource and Infrastructure for this task, where tools and applications are resources and they can be accessed and controlled via Web Services. The purpose of this paper is to present a method for modeling sessions in SAVOIR by using Web Ontology Language (OWL). We express in OWL 1.1 constraints on when sessions can and cannot be run or cannot be run concurrently with the ses- sions now running. There are several types of violations: aggregate bandwidth may exceed capacity, network infras- tructure may not be available, too many users may want to access a limited shared resource, etc. The session scheduler depends on the OWL 1.1 description logic reasoner to evaluate the session for violation of these constraints before the session is allowed to be scheduled.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.261
Teacher spread0.236 · 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 teacher head, 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

Citations2
Published2008
Admission routes1
Has abstractyes

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