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Record W2891828335 · doi:10.1109/cloud.2018.00128

ORGODEX: Service Portfolios for the Cloud

2018· article· en· W2891828335 on OpenAlexaff
Aaron Elliott, Scott Knight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCloud computingScalabilityComputer scienceSoftware deploymentPortfolioService (business)Distributed computingService catalogProcess managementComputer securityService delivery frameworkData scienceSoftware engineeringService designDatabaseBusinessOperating system

Abstract

fetched live from OpenAlex

The cloud computing model offers cost effective alternatives for large distributed organizations promoting service-oriented architectures and cloud first approaches to enable a modern workplace anywhere, anytime with anyone. In this work, our objective is to validate the broader applicability of the ORGODEX model and methodology and deliver a scalable service portfolio for the cloud. First, we analyze the services of large distributed organizations, demonstrating how roles, information, responsibilities and constraints may be used to maintain service portfolios. Next, we realize the deployment of secure cloud-based service portfolios. Finally, we produce comprehensible role and responsibility matrices, demonstrating administrative scalability.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2018
Admission routes1
Has abstractyes

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