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Record W4414882941 · doi:10.1051/epjconf/202533701146

Building the Key4hep Software Stack with Spack

2025· article· en· W4414882941 on OpenAlexaff
Juan Miguel Carceller, W. Deconinck, T. Madlener, André Sailer

Bibliographic record

VenueEPJ Web of Conferences · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsUniversity of Manitoba
FundersEuropean CommissionCERN
KeywordsSuiteStack (abstract data type)SoftwareCompilerSoftware suiteEvent (particle physics)Software frameworkSimple (philosophy)Software versioning

Abstract

fetched live from OpenAlex

The Key4hep software stack enables studies for future collider projects. It provides a full software suite for doing event generation, detector simulation as well as reconstruction and analysis. In the Key4hep stack, over 600 packages are built using the Spack package manager and deployed via the cvmfs software distribution system. In this contribution, we explain the current setup for building nightly builds and stable releases that are made every few months or as needed. These builds are made available to users, who have access to a full and consistent software stack via a simple setup script. Different operating systems and compilers are supported and some utilities are provided to make development on top of the Key4hep builds easier. Both the benefits of the community-driven approach followed in Spack and the issues found along the way are discussed.

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.

How this classification was reachedexpand

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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