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Record W4403581450 · doi:10.21203/rs.3.rs-5094199/v1

Brain Charts for the Rhesus Macaque Lifespan

2024· preprint· en· W4403581450 on OpenAlexaff
Ting Xu, Samuel Alldritt, Julian S.B. Ramirez, Reinder Vos de Wael, Richard A. I. Bethlehem, Jakob Seidlitz, Karl‐Heinz Nenning, Jonathan Smallwood, Zexi Wang, Nathália Bianchini Esper, Alexandre R. Franco, Aaron Alexander‐Bloch, David G. Amaral, Céline Amiez, Fabien Balezeau, Mark G. Baxter, Guillaume Becker, Jeffrey Bennett, Olivia Berkner, Erwin L. A. Blezer, Ansgar M. Brambrink, Thomas Brochier, Brent Butler, Kyoungseob Byeon, Lillian Campos, Emmanuelle Canet‐Soulas, Lucie Chalet, Aihua Chen, Justine Cléry, Christos Constantinidis, Douglas J. Cook, Stanislas Dehaene, Lena Dorfschmidt, Carly M. Drzewiecki, John W. Erdman, Stefan Everling, Arnaud Falchier, Lazar Fleysher, Andrew S. Fox, Winrich A. Freiwald, Mathilda Froesel, Seán Froudist‐Walsh, Julie L. Fudge, Thomas Funck, Maëva Gacoin, Daniel J. Gale, Clément M. Garin, Timothy D. Griffiths, Carole Guedj, Fadila Hadj‐Bouziane, Suliann Ben Hamed, Noam Harel, Renée Hartig, Katja Heuer, Bassem Hiba, Brittany Howell, Béchir Jarraya, Benjamin Jung, Ned H. Kalin, Joshua Karpf, Sabine Kästner, P. Christiaan Klink, Zsofia Kovacs‐Balint, Christopher D. Kroenke, Matthew J. Kuchan, Sze Chai Kwok, Kevin N. Laland, David A. Leopold, Gang Li, Patrik Lindenfors, Gary Linn, Rogier B. Mars, Kurt Masiello, Ravi S. Menon, Adam Messinger, Martine Meunier, Kelvin Mok, John H. Morrison, Jennifer Nacef, Jamie Nagy, Viola Neudecker, Martha Neuringer, MaryAnn P. Noonan, Michael Ortiz-Rios, Jose F. Perez‐Zoghbi, Mark A. Pinsk, Colline Poirier, Emmanuel Procyk, Reza Rajimehr, Simon M. Reader, David A. Rudko, Matthew F. S. Rushworth, Brian E. Russ, Jérôme Sallet, Mar M. Sánchez, Michael C. Schmid, Caspar M. Schwiedrzik, Julia A. Scott, Julien Sein, Keshov Sharma, Amir Shmuel, Martin Styner, Elinor L. Sullivan, Alexander Thiele, Orlin S. Todorov, Roberto Toro, Doris Y. Tsao, Anita Tusche, Roza Vlasova, Lei Wang, Zheng Wang, Jiaojian Wang, Alison R. Weiss, Charles Wilson, Essa Yacoub, Wilbert Zarco, Yong‐Di Zhou, Junda Zhu, Christopher I. Petkov, Daniel S. Margulies, Damien A. Fair, Charles M. Schroeder, Michael P. Milham

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMontreal Neurological Institute and HospitalWestern UniversityMcGill UniversityQueen's University
Fundersnot available
KeywordsMacaqueRhesus macaqueNeuroscienceBiologyPsychologyVirology

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)0.000
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.005

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 head0.196
GPT teacher head0.439
Teacher spread0.244 · 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
Published2024
Admission routes1
Has abstractno

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