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Open XAL Status Report 2015

2015· article· en· W2916926104 on OpenAlexaff
T. Pelaia

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2015
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsTRIUMF
Fundersnot available
KeywordsOak Ridge National LaboratorySpallation Neutron SourceOpen sourceNational laboratoryComputer scienceOperating systemPort (circuit theory)ArchitectureOpen architectureParticle acceleratorNeutron sourceEngineeringSoftwarePhysicsNuclear physicsNeutronEngineering physicsGeographyElectrical engineeringCivil engineering

Abstract

fetched live from OpenAlex

Open XAL is an accelerator physics software platform developed in collaboration among several facilities around the world. The Open XAL collaboration was formed in 2010 to port, improve and extend the successful XAL platform used at the Spallation Neutron Source for use in the broader accelerator community and to establish it as the standard platform for accelerator physics software. The site-independent core is complete, active applications have been ported, and now we are in the process of verification and transitioning to using Open XAL in production. This paper will present the current status and a roadmap for this project.

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 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)0.023
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.982
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0030.002
Scholarly communication0.0180.017
Open science0.0050.009
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1970.240

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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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.

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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