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Record W2503158277 · doi:10.1016/j.molcel.2016.06.030

Cerebellar Ataxia and Coenzyme Q Deficiency through Loss of Unorthodox Kinase Activity

2016· article· en· W2503158277 on OpenAlexfundno aff
Jonathan A. Stefely, Floriana Licitra, Leila Laredj, Andrew G. Reidenbach, Zachary A. Kemmerer, Anais Grangeray, Tiphaine Jaeg-Ehret, Catherine E. Minogue, Arne Ulbrich, Paul D. Hutchins, Emily Wilkerson, Zheng Ruan, Deniz Aydın, Alexander S. Hebert, Xiao Guo, Elyse C. Freiberger, Laurence Reutenauer, Adam Jochem, Maya Chergova, Isabel Johnson, Danielle C. Lohman, Matthew J. P. Rush, Nicholas W. Kwiecien, Pankaj Kumar Singh, A. Schlagowski, Brendan J. Floyd, Ulrika Forsman, Pavel Šindelář, Michael S. Westphall, Fabien Pierrel, Joffrey Zoll, Matteo Dal Peraro, Natarajan Kannan, C.A. Bingman, Joshua J. Coon, Philippe Isope, Hélène Puccio, David J. Pagliarini

Bibliographic record

VenueMolecular Cell · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCoenzyme Q10 studies and effects
Canadian institutionsnot available
FundersU.S. National Library of MedicineNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute on AgingFondation pour la Recherche MédicaleÉcole Polytechnique Fédérale de LausanneLabexInstitute of GeneticsMichigan Economic Development CorporationNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungU.S. Department of EnergyAgence Nationale de la RechercheEuropean Research CouncilNational Science Foundation
KeywordsBiologyAtaxiaCerebellar ataxiaKinaseCell biologyGeneticsNeuroscience

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations123
Published2016
Admission routes1
Has abstractno

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