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Record W2330589202 · doi:10.1109/mce.2014.2338496

How Data Centers Provide Consumer Services [The Art of Storage]

2014· article· en· W2330589202 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

VenueIEEE Consumer Electronics Magazine · 2014
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceCloud computingServerComputer data storageCloud storageData accessData centerThe InternetInternet of ThingsTelecommunicationsWorld Wide WebDatabaseComputer networkOperating system

Abstract

fetched live from OpenAlex

The mobile devices that we use every day, and that seemingly have unlimited access to information, have limited storage capacity and processing power (storage capacities are usually fewer than 128 GB). They are dependent upon digital storage and servers in remote data centers that are accessible over the Internet, often called the cloud. The usefulness of these mobile consumer products and the services they provide depend upon the enterprise infrastructure that supports them. This article will explore what goes on in these data centers and how these consumer services get delivered to us. So, let's take an inside look at these data centers and the networks they make possible to see how content gets to your consumer products.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.228
Teacher spread0.211 · 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