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Record W4412091574 · doi:10.1038/s43587-025-00931-0

Author Correction: Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration

2025· erratum· en· W4412091574 on OpenAlexaff
Rowan Saloner, Adam M. Staffaroni, Eric B. Dammer, Erik C. B. Johnson, Emily W. Paolillo, Amy B. Wise, Hilary W. Heuer, Leah K. Forsberg, Argentina Lario‐Lago, Julia D Webb, Jacob W. Vogel, Alexander Santillo, Oskar Hansson, Joel H. Kramer, Bruce L. Miller, Jingyao Li, Joseph Loureiro, Rajeev Sivasankaran, Kathleen A. Worringer, Nicholas T. Seyfried, Jennifer S. Yokoyama, Salvatore Spina, Lea T. Grinberg, William W. Seeley, Lawren VandeVrede, Peter A. Ljubenkov, Ece Bayram, Andrea Bozoki, Danielle Brushaber, Ciaran Considine, Gregory S. Day, Bradford C. Dickerson, Kimiko Domoto‐Reilly, Kelley Faber, Douglas Galasko, Tania F. Gendron, Daniel H. Geschwind, Nupur Ghoshal, Caroline Graff, Chadwick M. Hales, Lawrence S. Honig, Ging‐Yuek Robin Hsiung, Edward D. Huey, John Kornak, Walter K. Kremers, Maria I. Lapid, Suzee E. Lee, Irene Litvan, Corey T. McMillan, Mario F. Mendez, Toji Miyagawa, Alexander Pantelyat, Belén Pascual, Joseph C. Masdeu, Henry L. Paulson, Leonard Petrucelli, Peter Pressman, Rosa Rademakers, Eliana Marisa Ramos, Katya Rascovsky, Erik D. Roberson, Rodolfo Savica, Allison Snyder, Anna Campbell Sullivan, Maria Carmela Tartaglia, Marijne Vandebergh, Bradley F. Boeve, Howie Rosen, Julio C. Rojas, Adam L. Boxer, Kaitlin B. Casaletto

Bibliographic record

VenueNature Aging · 2025
Typeerratum
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsOccupational Cancer Research CentreUniversity of TorontoUniversity of British Columbia
FundersNational Institute on Aging
KeywordsFrontotemporal lobar degenerationCerebrospinal fluidProteomeScale (ratio)PathologyMedicineNeuroscienceBioinformaticsFrontotemporal dementiaBiologyGeographyCartographyDisease

Abstract

fetched live from OpenAlex

This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.005
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0580.033

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 head0.005
GPT teacher head0.256
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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