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Record W4401993448 · doi:10.1038/s41390-024-03446-3

Correction: A recommendation for the use of electrical biosensing technology in neonatology

2024· erratum· en· W4401993448 on OpenAlexaff
Lizelle van Wyk, Topun Austin, Bernard Barzilay, María Carmen Bravo, Morten Breindahl, Christoph Czernik, Eugene Dempsey, Willem P. de Boode, Willem de Vries, Beate Horsberg Eriksen, Jean‐Claude Fauchère, Elisabeth M. W. Kooi, Philip T. Levy, Patrick J. McNamara, Subhabrata Mitra, Eirik Nestaas, Heike Rabe, Yacov Rabi, Sheryle Rogerson, Marilena Savoia, Frederico Schena, Arvind Sehgal, Christoph E. Schwarz, Ulrich Thomé, David Van Laere, Gabriela Zaharie, Samir Gupta

Bibliographic record

VenuePediatric Research · 2024
Typeerratum
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNeonatologyMedicineMedical physicsIntensive care medicineComputer scienceComputational biologyBiologyGeneticsPregnancy

Abstract

fetched live from OpenAlex

In the original version of the article, the author’s name Christoph E. Schwarz was incorrectly given as ‘Christop E. Schwarz’. The author’s name Arvind Sehgal was incorrectly given as ‘Arvind Seghal’. The original article has been corrected.

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 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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.194
GPT teacher head0.398
Teacher spread0.204 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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