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Record W4379054492 · doi:10.1038/s41390-023-02655-6

Correction: Improving clinical paediatric research and learning from COVID-19: recommendations by the Conect4Children expert advice group

2023· erratum· en· W4379054492 on OpenAlexaff
Athimalaipet V Ramanan, Neena Modi, Saskia N. de Wildt, Beate Aurich, Sophia Bakhtadze, Francisco Bautista, Fernando Cabañas, Lisa Campbell, Michela Casanova, Philippa A. Charlton, Wallace Crandall, Irmgard Eichler, Laura Fregonese, Daniel B. Hawcutt, Pablo Iveli, Thomas Jaki, Bosanka Jocić-Jakubi, Mats Johnson, Florentia Kaguelidou, Bülent Karadağ, Lauren E. Kelly, Ming Yann Lim, Carmen Moreno, Eva Neumann, Cécile Ollivier, Mehdi Oualha, Genny Raffaeli, Maria Alexandra Ribeiro, Emmanuel Roilides, Teresa de Rojas, Alba Rubio‐San‐Simón, Nicolino Ruperto, Maurizio Scarpa, Matthias Schwab, Angeliki Siapkara, Yogen Singh, Anne Smits, Pasquale Striano, Silvana Anna Maria Urru, Marina Vivarelli, Zorica Zivkoviz

Bibliographic record

VenuePediatric Research · 2023
Typeerratum
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsAdvice (programming)Coronavirus disease 2019 (COVID-19)Medical education2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Group (periodic table)PsychologyMedicineFamily medicineComputer scienceVirologyPathologyChemistry

Abstract

fetched live from OpenAlex

“The publication reflects the author’s view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained therein.”

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)0.019
metaresearch head score (Gemma)0.210
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.210
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0050.005
Scholarly communication0.0080.004
Open science0.0050.004
Research integrity0.0200.032
Insufficient payload (model declined to judge)0.0650.064

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 head0.270
GPT teacher head0.554
Teacher spread0.283 · 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.

Study designNot applicable
DomainMethods
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
Published2023
Admission routes1
Has abstractyes

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