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Record W2898836903 · doi:10.1038/s41467-018-07153-6

Publisher Correction: Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease

2018· erratum· en· W2898836903 on OpenAlexaff
Rached Alkallas, Lisa Fish, Hani Goodarzi, Hamed S. Najafabadi

Bibliographic record

VenueNature Communications · 2018
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsMcGill Genome CentreMcGill UniversityMcGill University and Génome Québec Innovation Centre
Fundersnot available
KeywordsInferenceLegendComputer scienceProfiling (computer programming)Computational biologyArtificial intelligenceMedicineBiologyHistoryArt history

Abstract

fetched live from OpenAlex

The original version of this Article contained an error in Figure 3, where panel d was inadvertently replaced with a duplicate of panel c during typesetting. Also, the legend of Figure 5f incorrectly read '310 AD patients (blue dots, r = -0.4) and 157 non-demented individuals (green dots, r = -0.1)', and should have read '310 AD patients (blue dots, r = -0.1) and 157 non-demented individuals (green dots, r = -0.4)'. Both of these errors have now been corrected in both the PDF and HTML 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)0.004
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 score0.062
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0620.033

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 head0.026
GPT teacher head0.291
Teacher spread0.265 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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