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Record W7009548595

The environmental impact, carbon emissions and sustainability of computing in the ATLAS experiment

2025· article· en· W7009548595 on OpenAlexfundno aff

Bibliographic record

VenueDESY (CERN, DESY, Fermilab, IHEP, and SLAC) · 2025
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaInstitut National de Physique Nucléaire et de Physique des ParticulesAgencia Nacional de Promoción Científica y TecnológicaScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y DesarrolloForskningsrådet om Hälsa, Arbetsliv och VälfärdNational Science and Technology CouncilCentre National pour la Recherche Scientifique et TechniqueCentre National de la Recherche ScientifiqueMax-Planck-GesellschaftBundesministerium für Wissenschaft, Forschung und WirtschaftAustrian Science FundEuropean Regional Development FundBundesministerium für Bildung und ForschungMinisterstvo Školství, Mládeže a TělovýchovyJapan Society for the Promotion of ScienceConselho Nacional de Desenvolvimento Científico e TecnológicoU.S. Department of EnergyNational Natural Science Foundation of ChinaFundação de Amparo à Pesquisa do Estado de São PauloH2020 Marie Skłodowska-Curie ActionsAgencia Estatal de InvestigaciónSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMinistry of Education, Culture, Sports, Science and TechnologyNederlandse Organisatie voor Wetenschappelijk OnderzoekAgence Nationale de la RechercheEuropean CommissionDanmarks GrundforskningsfondTRIUMFAlexander von Humboldt-StiftungBritish Columbia Knowledge Development FundTürkiye Enerji, Nükleer ve Maden Araştırma KurumuMinisterstwo Edukacji i NaukiCERNDeutsche ForschungsgemeinschaftNational Science FoundationUK Research and InnovationCanarie
KeywordsSustainabilityGreenhouse gasAtlas (anatomy)Climate changeSustainable developmentCarbon fibers
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.236
Teacher spread0.232 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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