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Record W2398620219

The 7th CASCON workshop on cloud computing

2015· article· en· W2398620219 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Science and Software Engineering · 2015
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsYork University
Fundersnot available
KeywordsCloud computingIBMComputer scienceSoftware as a serviceMiddleware (distributed applications)Cloud computing securitySingle-chip Cloud ComputerOperating systemSoftwareService (business)Cloud testingWorkloadServerComputer securityDatabaseSoftware development
DOInot available

Abstract

fetched live from OpenAlex

Hybrid clouds are private and public sub-clouds working together to mitigate privacy and security concerns while addressing the need for large computation and storage capacity. Academic research into hybrid clouds has focused on the middleware and abstraction layers for creating, managing, and using hybrid clouds. For example, researchers used the MapReduce paradigm to split a data-intensive workload into mapping tasks sorted by the sensitivity of the data, with the most sensitive data being processed locally and the least sensitive processed in a public cloud. Commercial support for hybrid clouds is growing in response to the business case for cloud federation. IBM offers both PureApplication System (to manage a private cloud) and PureApplication Service (a public cloud offering) and software to bridge the two at the Software-as-a-Service (SaaS) level. More recently, IBM Blue Mix Platform-as-a-Service (PaaS) enables integration of IBM Blue Mix cloud with on-premises private clouds.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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