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Record W2784545598 · doi:10.1016/j.ajhg.2017.12.015

Functional Dysregulation of CDC42 Causes Diverse Developmental Phenotypes

2018· article· en· W2784545598 on OpenAlexaff
Simone Martinelli, Oliver H.F. Krumbach, Francesca Pantaleoni, Simona Coppola, Ehsan Amin, Luca Pannone, Kazem Nouri, Luciapia Farina, Radovan Dvorský, Francesca Romana Lepri, Marcel Buchholzer, Raphael Konopatzki, Laurence E. Walsh, Katelyn Payne, Mary Ella Pierpont, Samantha A. Schrier Vergano, Katherine G. Langley, Douglas P. Larsen, Kelly D. Farwell, Sha Tang, Cameron Mroske, Ivan Gallotta, Elia Di Schiavi, Matteo Della Monica, Licia Lugli, Cesare Rossi, Marco Seri, Guido Cocchi, Lindsay B. Henderson, Berivan Baskin, Mariëlle Alders, Roberto Mendoza‐Londono, Lucie Dupuis, Deborah A. Nickerson, Jessica X. Chong, Naomi Meeks, Kathleen Brown, Tahnee N. Causey, Megan T. Cho, Stephanie Demuth, M. Cristina Digilio, Bruce D. Gelb, Michael J. Bamshad, Martin Zenker, Mohammad Reza Ahmadian, Raoul C. M. Hennekam, Marco Tartaglia, Ghayda Mirzaa

Bibliographic record

VenueThe American Journal of Human Genetics · 2018
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersOffice of Research Infrastructure Programs, National Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeNational Human Genome Research InstituteMinistero della SaluteNational Heart, Lung, and Blood InstituteCenter for Mendelian Genomics, University of WashingtonNational Institutes of HealthAssociazione Italiana per la Ricerca sul CancroUniversity of WashingtonHeinrich-Heine-Universität DüsseldorfFondazione Bambino GesùE-Rare
KeywordsPhenotypeCDC42NeurosciencePsychologyBiologyGeneticsGeneSignal transduction

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 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 score0.563
Threshold uncertainty score0.493

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.277
Teacher spread0.245 · 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 teacher head, 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

Citations186
Published2018
Admission routes1
Has abstractno

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