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Record W2934716016 · doi:10.1016/j.ajhg.2019.03.001

Truncating Mutations in UBAP1 Cause Hereditary Spastic Paraplegia

2019· article· en· W2934716016 on OpenAlex
Mohammad Ali Farazi Fard, Adriana Rebelo, Elena Buglo, Hamid Nemati, Hassan Dastsooz, Ina Gehweiler, Selina Reich, Jennifer Reichbauer, Beatriz Quintáns, Andrés Ordóñez‐Ugalde, Andrea Cortese, Steve Courel, Lisa Abreu, Eric Powell, Matt C. Danzi, Nicole Belliard Martuscelli, Dana M. Bis‐Brewer, Feifei Tao, Fariba Zarei, Parham Habibzadeh, Majid Yavarian, Farzaneh Modarresi, Mohammad Silawi, Zahra Tabatabaei, Masoume Yousefi, Hamid Reza Farpour, Christoph Keßler, Elisabeth Mangold, Xenia Kobeleva, Ivailo Tournev, Teodora Chamova, Amelie J. Mueller, Tobias B. Haack, Mark A. Tarnopolsky, Ziv Gan‐Or, Guy A. Rouleau, Matthis Synofzik, María‐Jesús Sobrido, Albena Jordanova, Rebecca Schüle, Stephan Züchner, Mohammad Ali Faghihi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe American Journal of Human Genetics · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalMcMaster University
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthHorizon 2020Instituto de Salud Carlos IIIUniversiteit AntwerpenFederación Española de Enfermedades RarasBundesministerium für Bildung und ForschungMorris Animal Foundation
KeywordsHereditary spastic paraplegiaParaplegiaSpasticMedicinePhysical medicine and rehabilitationMutationGeneticsBiologySpinal cordPhenotypeCerebral palsyGenePsychiatry

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.297
Teacher spread0.259 · 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