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Record W2681714461 · doi:10.1038/ncb3563

AML1-ETO requires enhanced C/D box snoRNA/RNP formation to induce self-renewal and leukaemia

2017· article· en· W2681714461 on OpenAlexaff
Fengbiao Zhou, Yi Liu, Christian Rohde, Cornelius Pauli, Dennis Gerloff, Marcel Köhn, Danny Misiak, Nicole Bäumer, Chunhong Cui, Stefanie Göllner, Thomas Oellerich, Hubert Serve, María-Paz García-Cuéllar, Robert K. Slany, Jaroslaw P. Maciejewski, Bartlomiej Przychodzen, Barbara Seliger, Hans‐Ulrich Klein, Christoph Bartenhagen, Wolfgang E. Berdel, Martin Dugas, Makoto M. Taketo, Daneyal Farouq, Schraga Schwartz, Aviv Regev, Josée Hébert, Guy Sauvageau, Caroline Pabst, Stefan Hüttelmaier, Carsten Müller‐Tidow

Bibliographic record

VenueNature Cell Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsInstitute for Research in Immunology and CancerUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersBroad Institute
KeywordsSmall nucleolar RNABiologyCell biologyRibonucleoproteinMethylationRNACancer researchMolecular biologyLong non-coding RNAGeneticsGene

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.282
Teacher spread0.275 · 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

Citations192
Published2017
Admission routes1
Has abstractno

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