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Record W6999575979

Comparison of inclusive and photon-tagged jet suppression in 5.02 TeV Pb+Pb collisions with ATLAS

2023· article· en· W6999575979 on OpenAlexfundno aff

Bibliographic record

VenueRepositóriUM (Universidade do Minho) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
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 DesarrolloSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungJavna Agencija za Raziskovalno Dejavnost RSMax-Planck-GesellschaftIsrael Science FoundationCentre National pour la Recherche Scientifique et TechniqueBundesministerium für Wissenschaft, Forschung und WirtschaftMinistry of Education, Culture, Sports, Science and TechnologyNederlandse Organisatie voor Wetenschappelijk OnderzoekAustrian Science FundCentre National de la Recherche ScientifiqueBundesministerium für Bildung und ForschungMinisterstvo Školství, Mládeže a TělovýchovyBritish Columbia Knowledge Development FundTürkiye Enerji, Nükleer ve Maden Araştırma KurumuDanmarks GrundforskningsfondCERNJapan Society for the Promotion of ScienceConselho Nacional de Desenvolvimento Científico e TecnológicoU.S. Department of EnergyNational Natural Science Foundation of ChinaNational Science FoundationCompute CanadaMinisterio de Ciencia e InnovaciónCanarieFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPartonAtlas (anatomy)High energyJet (fluid)PhotonDistribution functionLarge Hadron ColliderAtlas detector
DOInot available

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.015
GPT teacher head0.305
Teacher spread0.291 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractno

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