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Record W1992506382 · doi:10.1016/j.future.2006.12.006

GridX1: A Canadian computational grid

2007· article· en· W1992506382 on OpenAlexafffundabout
Arun Agarwal, Mohamed Ben Ahmed, Avery Berman, B. Caron, André Charbonneau, Denice Deatrich, Ron Desmarais, Alexandros Dimopoulos, Ian Gable, L. Groer, R. Haria, Roger Impey, L. Klektau, C. Lindsay, Gabriel Mateescu, Quinn Matthews, Alexa Norton, W Podaima, Darcy Quesnel, Rob Simmonds, R. Sobie, Bill St. Arnaud, C. Usher, Daniel C. Vanderster, M. C. Vetterli, R. Walker, Marco Yuen

Bibliographic record

VenueFuture Generation Computer Systems · 2007
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsSimon Fraser UniversityUniversity of TorontoTRIUMFCanarieNational Research Council CanadaUniversity of AlbertaUniversity of CalgaryUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaCERN
KeywordsComputer scienceGridGrid computingDistributed computingJob schedulerLoad balancing (electrical power)Fault toleranceScheduling (production processes)Overhead (engineering)Computer clusterLarge Hadron ColliderDatabaseOperating systemCloud computing

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0050.001
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0660.013

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2007
Admission routes3
Has abstractno

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