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Record W2167233120 · doi:10.1109/mdso.2006.66

Grid Computing Gets Small

2006· article· en· W2167233120 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 Distributed Systems Online · 2006
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceInteroperabilityProvisioningGrid computingGridMiddleware (distributed applications)Bandwidth (computing)Interface (matter)Semantic gridCloud computingWorld Wide WebTelecommunicationsDistributed computingOperating systemSemantic Web

Abstract

fetched live from OpenAlex

The US and Japan have successfully demonstrated one of grid computing's long-standing holy grails - dynamic, on-demand provisioning of bandwidth and interoperability between high-performance resources in two national research testbeds. The automated interoperability between Japan's G-lambda project and the US's Enlightened Computing project was demonstrated 11 September at the annual Global LambdaGrid Workshop (http://news.ncsu.edu/releases/2006/sept/documents/global lowbargrid.pdf) in Tokyo. The demonstration featured some of the most advanced research facilities in both nations, highlighting new middleware capable of reliably coordinating both network and computational resources as well as other protocol and interface technologies. Advances in grid computing technology have tended to focus on large-scale research deployments like this one, but smaller deployments are beginning to get headlines as well. This shift could change the way we view this field-as long as grid architects are willing to expand their vision of a grid beyond raw network speed and CPU aggregation

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.020
GPT teacher head0.242
Teacher spread0.221 · 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