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Record W4409119715 · doi:10.1007/s40279-025-02185-7

Correction to: Improving National and International Surveillance of Movement Behaviours in Childhood and Adolescence: An International Modified Delphi Study

2025· erratum· en· W4409119715 on OpenAlexaff
John J. Reilly, Rachel Andrew, Chalchisa Abdeta, Liane B. Azevedo, Nicolás Aguilar-Farías, Sharon Barak, Farid Bardid, Bruno Bizzozero‐Peroni, Javier Brazo‐Sayavera, Jonathan Y. Cagas, Mohamed Souhaiel Chelly, Lars Breum Christiansen, Visnja Djordjic, Catherine E. Draper, Asmaa El Hamdouchi, Elie-Jacques Fares, Aleš Gába, Kylie D. Hesketh, Mohammad Sorowar Hossain, Yajun Huang, Alejandra Jáuregui, Sanjay Juvekar, Nicholas Kuzik, Richard Larouche, Eun‐Young Lee, Sharon Levi, Yang Liu, Marie Löf, Tom Loney, José Francisco López‐Gil, Evelin Mäestu, Taru Manyanga, Clarice Martins, María Mendoza-Muñoz, Shawnda A. Morrison, Nyaradzai Munambah, Tawonga Mwase‐Vuma, Rowena Naidoo, Reginald T-A. Ocansey, Anthony D. Okely, Aoko Oluwayomi, Susan Paudel, Bee Koon Poh, Evelyn Helena Corgosinho Ribeiro, Diego Augusto Santos Silva, Mohd Razif Shahril, Melody Smith, Amanda E. Staiano, Martyn Standage, Narayan Subedi, Chiaki Tanaka, Hong Tang, David Thivel, Mark S. Tremblay, Edin Užičanin, Dimitris Vlachopoulos, E. Kipling Webster, Dyah Anantalia Widyastari, Paweł Zembura, Salomé Aubert

Bibliographic record

VenueSports Medicine · 2025
Typeerratum
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsHealth CanadaUniversity of Northern British ColumbiaUniversity of LethbridgeQueen's UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsDelphi methodSports medicineDelphiMedicinePsychologyApplied psychologyPolitical scienceEnvironmental healthMedical educationPhysical therapyComputer science

Abstract

fetched live from OpenAlex

Correction to: Improving National and International Surveillance of Movement Behaviours in Childhood and Adolescence: An International Modified Delphi Study.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.309
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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