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Record W2071102560 · doi:10.1016/j.future.2006.03.009

The global lambda visualization facility: An international ultra-high-definition wide-area visualization collaboratory

2006· article· en· W2071102560 on OpenAlexafffund
Jason Leigh, Luc Renambot, Andrew Johnson, Byungil Jeong, Ratko Jagodic, Nicholas Schwarz, Dmitry Svistula, Rajvikram Singh, Julieta Aguilera, Xi Wang, Venkatram Vishwanath, Brenda López, Dan Sandin, Tom Peterka, Javier Girado, Robert Kooima, Jinghua Ge, Lance Long, Alan Verlo, Thomas A. DeFanti, Maxine Brown, Donna Cox, Robert Patterson, Patrick Dorn, P. J. Wefel, Stuart Levy, Jonas Talandis, Joe Reitzer, Tom Prudhomme, Tom Coffin, Brian Davis, Paul Wielinga, Bram Stolk, Gee-bum Koo, Jaeyoun Kim, Sang Woo Han, Jong‐Won Kim, Brian Corrie, Todd Zimmerman, Pierre Boulanger, Manuel García

Bibliographic record

VenueFuture Generation Computer Systems · 2006
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsUniversity of AlbertaSimon Fraser University
FundersOffice of Naval ResearchBritish Columbia Knowledge Development FundUniversity of Illinois at ChicagoUniversity of California, San DiegoUniversité LavalUniversity of Illinois at Urbana-ChampaignSimon Fraser UniversityKorea Institute of Science and Technology InformationGwangju Institute of Science and TechnologyNational Research Council CanadaCanarieUniversity of AlbertaNational Science Foundation
KeywordsCollaboratoryVisualizationComputer scienceSoftwareGridWorld Wide WebSoftware engineeringHuman–computer interactionOperating systemData mining

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)0.009
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0040.004
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0360.009

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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
GenreEmpirical

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

Citations30
Published2006
Admission routes2
Has abstractno

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