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GAMBIT: the global and modular beyond-the-standard-model inference tool

2017· article· en· W2619880418 on OpenAlexafffund
Peter Athron, Csaba Balázs, Torsten Bringmann, A. G. Buckley, M. Chrząszcz, J. M. Conrad, Jonathan M. Cornell, Lars A. Dal, H. J. Dickinson, Joakim Edsjö, Ben Farmer, Tomás E. Gonzalo, P. Jackson, Abram Krislock, Anders Kvellestad, Johan Lundberg, James McKay, F. Mahmoudi, Gregory D. Martinez, Antje Putze, Are Raklev, Joachim Ripken, Christopher Sean Rogan, Aldo Saavedra, Christopher Savage, Pat Scott, N. Serra, Christoph Weniger, M. J. White, Sebastian Wild

Bibliographic record

VenueThe European Physical Journal C · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsMcGill University
FundersH2020 Marie Skłodowska-Curie ActionsAcademic Computer Centre Cyfronet, AGH University of Science and TechnologyFonds de recherche du Québec – Nature et technologiesAustralian Research CouncilScience and Technology Facilities CouncilHorizon 2020 Framework ProgrammeNorges ForskningsrådNatural Sciences and Engineering Research Council of CanadaKnut och Alice Wallenbergs StiftelseNederlandse Organisatie voor Wetenschappelijk OnderzoekEuropean CommissionNational Science FoundationRoyal SocietyInfrastruktura PL-GridUniversity of SydneySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungBanff International Research Station for Mathematical Innovation and DiscoveryUniversity of GlasgowVetenskapsrådet
KeywordsGambitModular designComputer scienceInferenceInterfacingTrinomialProgramming languageSimulationArtificial intelligenceMathematicsComputer hardware

Abstract

fetched live from OpenAlex

We describe the open-source global fitting package GAMBIT: the Global And Modular Beyond-the-Standard-Model Inference Tool. GAMBIT combines extensive calculations of observables and likelihoods in particle and astroparticle physics with a hierarchical model database, advanced tools for automatically building analyses of essentially any model, a flexible and powerful system for interfacing to external codes, a suite of different statistical methods and parameter scanning algorithms, and a host of other utilities designed to make scans faster, safer and more easily-extendible than in the past. Here we give a detailed description of the framework, its design and motivation, and the current models and other specific components presently implemented in GAMBIT. Accompanying papers deal with individual modules and present first GAMBIT results. GAMBIT can be downloaded from gambit.hepforge.org .

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.019
GPT teacher head0.298
Teacher spread0.279 · 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.

Study designTheoretical or conceptual
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

Citations124
Published2017
Admission routes2
Has abstractyes

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