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Record W7103533304

Differential tt¯ cross-section measurements using boosted top quarks in the all-hadronic final state with 139 fb<sup> −1</sup> of ATLAS data

2023· article· en· W7103533304 on OpenAlexfundno aff

Bibliographic record

VenueApollo (University of Cambridge) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
FundersSLAC National Accelerator LaboratoryAgencia Nacional de Promoción Científica y TecnológicaH2020 Marie Skłodowska-Curie ActionsUniversity of Illinois at Urbana-ChampaignInstitut National de Physique Nucléaire et de Physique des ParticulesStony Brook UniversityAgencia Nacional de Investigación y DesarrolloSorbonne UniversitéPontificia Universidad Católica de ChileUniversidade do MinhoUniversidad de TarapacáUniversité Paris-SaclayUniverzita Palackého v OlomouciUniversity of TsukubaAristotle University of ThessalonikiTechnion-Israel Institute of TechnologyNarodowa Agencja Wymiany AkademickiejUnited Arab Emirates UniversityJapan Society for the Promotion of ScienceUniversidade de CoimbraUniversitetet i OsloMinisterio de Ciencia e InnovaciónJavna Agencija za Raziskovalno Dejavnost RSSimon Fraser UniversityUniversidad de GranadaKungliga Tekniska HögskolanUniversity of California, IrvineNew York University Abu DhabiIsrael Science FoundationUniversity of TorontoGeneralitat ValencianaGeneralitat de CatalunyaAcademia SinicaNatural Sciences and Engineering Research Council of CanadaUniversity of OregonMinistry of Education, Culture, Sports, Science and TechnologyBundesministerium für Bildung und ForschungMinisterstvo Školství, Mládeže a TělovýchovyLeverhulme TrustEuropean CommissionFundação de Amparo à Pesquisa do Estado de São PauloDanmarks GrundforskningsfondInstituto Superior TécnicoDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekDivision of PhysicsAgence Nationale de la RechercheUniversidade Federal de São João del-ReiUniversity of SussexEuropean Regional Development FundBritish Columbia Knowledge Development FundCentre National de la Recherche ScientifiqueMax-Planck-GesellschaftEuropean Social FundCentre National pour la Recherche Scientifique et TechniqueRoyal SocietyScience and Technology Facilities CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungYork UniversityCentres de Recerca de CatalunyaCERNUniversidad Técnica Federico Santa MaríaAkademie Věd České RepublikyBundesministerium für Wissenschaft, Forschung und WirtschaftUniversität SiegenCanarieTel Aviv UniversityUniversity of PittsburghJulius-Maximilians-Universität WürzburgCompute CanadaUniversity of OxfordAlexander von Humboldt-StiftungHigh Energy PhysicsUniversity of OklahomaTRIUMFUniverzita Karlova v PrazeConselho Nacional de Desenvolvimento Científico e TecnológicoOklahoma State UniversityNational Science FoundationUniversidade de LisboaNorthern Illinois UniversityFundação para a Ciência e a TecnologiaUniversity of WashingtonTürkiye Enerji, Nükleer ve Maden Araştırma KurumuAustrian Science FundUniversity of PennsylvaniaČeské Vysoké Učení Technické v PrazeOhio State UniversityNational Natural Science Foundation of ChinaU.S. Department of Energy
KeywordsHadronizationPartonPhase spaceHadronQuarkAtlas (anatomy)DetectorFiducial markerLarge Hadron Collider
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 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.082
GPT teacher head0.304
Teacher spread0.222 · 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 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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