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Record W4243819229 · doi:10.21203/rs.3.rs-537397/v2

Using genetics to uncouple higher adiposity from its adverse metabolic effects and understand its role in metabolic and non-metabolic disease.

2021· preprint· en· W4243819229 on OpenAlexafffund
Susan F. Martin, Jessica Tyrrell, E. Louise Thomas, Matthew J. Bown, Andrew R. Wood, Robin N. Beaumont, Lam C. Tsoi, Philip E. Stuart, James T. Elder, Philip Law, Richard S. Houlston, Christopher Kabrhel, Nikos Papadimitriou, Marc J. Gunter, Caroline J. Bull, Joshua A. Bell, Emma E. Vincent, Naveed Sattar, Malcolm G. Dunlop, Ian Tomlinson, Jimmy D. Bell, Timothy M. Frayling, Hanieh Yaghootkar

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsInstitute of Cancer Research
FundersNational Cancer InstituteInstituto de Salud Carlos IIIAgència de Gestió d'Ajuts Universitaris i de RecercaCancer Council VictoriaOntario Ministry of Research and InnovationNational Health and Medical Research CouncilWorld Cancer Research FundMedical Research CouncilCanadian Institutes of Health ResearchCenters for Disease Control and PreventionChonnam National University Hwasun HospitalHellenic Health FoundationBiobanco VascoJunta de Castilla y LeónBrigham and Women's HospitalWereld Kanker Onderzoek FondsWorld Health OrganizationXarxa de Bancs de Tumors de CatalunyaXunta de GaliciaDeutsche KrebshilfeAssociazione Italiana per la Ricerca sul CancroNordForskVetenskapsrådetMutuelle Générale de l'Education NationaleBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadCancerfondenUniversity of CambridgeZonMwCanadian Cancer Society Research InstituteInstitut National de la Santé et de la Recherche MédicaleChonnam National UniversityCentres de Recerca de CatalunyaUmeå UniversitetMinisterstvo Zdravotnictví Ceské RepublikyEuropean CommissionGeneralitat de CatalunyaFood Standards AgencyGénome QuébecInstitut Gustave-RoussyGrantová Agentura České RepublikyJohns Hopkins UniversityCentre International de Recherche sur le CancerLigue Contre le CancerDeutsches KrebsforschungszentrumHarvard T.H. Chan School of Public HealthFlorida Department of HealthNational Institutes of HealthMike and Josie Harper Cancer Research InstituteUniversity of PittsburghUniversity of ExeterWorld Cancer Research Fund InternationalMemorial Sloan-Kettering Cancer CenterMcGill UniversityU.S. Department of Health and Human ServicesAmerican Institute for Cancer ResearchDamon Runyon Cancer Research FoundationKWF KankerbestrijdingWageningen University and ResearchCancer Research UKInnovative Medicines InitiativeAmerican Cancer SocietyUniversity of South Florida
KeywordsMetabolic diseaseDiseaseMetabolic syndromeGeneticsMedicineObesityBiologyBioinformaticsInternal medicine

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.363
Teacher spread0.314 · 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 designObservational
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
Published2021
Admission routes2
Has abstractno

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