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Record W4386442965 · doi:10.1038/s41467-023-41255-0

Author Correction: In vivo CRISPR screens reveal Serpinb9 and Adam2 as regulators of immune therapy response in lung cancer

2023· erratum· en· W4386442965 on OpenAlexaff
Dzana Dervovic, Ahmad Malik, Edward L.Y. Chen, Masahiro Narimatsu, Nina Adler, Somaieh Afiuni‐Zadeh, Dagmar Krenbek, Sébastien Martinez, Ricky Tsai, Jonathan Boucher, Jacob M. Berman, Katie Teng, Arshad Ayyaz, YiQing Lü, Geraldine Mbamalu, Sampath K. Loganathan, Jongbok Lee, Li Zhang, Cynthia J. Guidos, Jeffrey L. Wrana, Arschang Valipour, Philippe P. Roux, Jüri Reimand, Hartland W. Jackson, Daniel Schramek

Bibliographic record

VenueNature Communications · 2023
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsMcGill UniversityUniversity of CalgaryUniversité de MontréalInstitute for Research in Immunology and CancerOntario Institute for Cancer ResearchToronto General HospitalUniversity Health NetworkUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsCRISPRIn vivoImmune systemLung cancerCancerBiologyCancer researchComputational biologyMedicineImmunologyPathologyBiotechnologyGeneticsGene

Abstract

fetched live from OpenAlex

The original sentence “Cells were then washed twice by centrifuging through PBS (800 × g , 5′) prior to re-suspending in PBS with 4-element EQ normalization beads (Standard BioTools: 201078)” should have read “Cells were then washed twice by centrifuging through PBS at 800 × g , 5’ prior to re-suspending in Maxpar Cell Acquisition Solution (Standard BioTools: 201241) with 4-element EQ normalization beads (Standard BioTools: 201078)”.

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)0.003
metaresearch head score (Gemma)0.032
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 score0.050
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0500.029

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 head0.011
GPT teacher head0.324
Teacher spread0.312 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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