MétaCan
Menu
Back to cohort
Record W3043214029 · doi:10.1016/j.mad.2020.111316

Lack of consensus on an aging biology paradigm? A global survey reveals an agreement to disagree, and the need for an interdisciplinary framework

2020· article· en· W3043214029 on OpenAlexafffund
Alan A. Cohen, Brian K. Kennedy, Ulrich Anglas, Anne M. Bronikowski, Joris Deelen, Frédérik Dufour, Gerardo Ferbeyre, Luigi Ferrucci, Claudio Franceschi, Daniela Frasca, Bertrand Friguet, Pierrette Gaudreau, Vadim N. Gladyshev, Efstathios S. Gonos, Vera Gorbunova, Philipp Gut, Mikhail Ivanchenko, Véronique Legault, Jean‐François Lemaître, Thomas Liontis, Guang‐Hui Liu, Mingxin Liu, Andrea B. Maier, Otávio de Tolêdo Nóbrega, Marcel G. M. Olde Rikkert, Graham Pawelec, Sylvie Rheault, Alistair M. Senior, Andreas Simm, Sonja K. Soo, Annika Traa, Svetlana Ukraintseva, Quentin Vanhaelen, Jeremy M. Van Raamsdonk, Jacek M. Witkowski, Anatoliy I. Yashin, Robert Ziman, Tamàs Fülöp

Bibliographic record

VenueMechanisms of Ageing and Development · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsHealth Sciences NorthInstitut Universitaire de Gériatrie de MontréalCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité de MontréalMcGill University Health CentreUniversité de Sherbrooke
FundersNational Institute on AgingNational Institutes of HealthAustralian Research CouncilCanadian Institutes of Health ResearchConselho Nacional de Desenvolvimento Científico e TecnológicoNational Institute of General Medical SciencesMinistry of Education and Science of the Russian Federation
KeywordsField (mathematics)PsychologyEpistemology

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)0.121
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.639

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.110
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.008
Science and technology studies0.0050.013
Scholarly communication0.0110.012
Open science0.0040.012
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0050.001

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 head0.054
GPT teacher head0.335
Teacher spread0.281 · 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 designObservational
DomainMethods
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

Citations135
Published2020
Admission routes2
Has abstractno

Explore more

Same venueMechanisms of Ageing and DevelopmentSame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207