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Record W4317791671 · doi:10.1016/j.jesf.2023.01.003

Corrigendum to “Physical activity in the era of climate change and COVID-19 pandemic: Results from the South Korea's 2022 Report Card on physical activity for children and adolescents” [J Exercise Sci Fitness 21(1) (2023) 26–33]

2023· erratum· en· W4317791671 on OpenAlexaff
Eun‐Young Lee, Yeong-Bae Kim, Seon Young Goo, Okimitsu Oyama, Jeongmin Lee, Geonhui Kim, Heejun Lim, Hoyong Sung, Jiyeon Yoon, Jongnam Hwang, Sochung Chung, Hyun Joo Kang, Joon Young Kim, Kwon-il Kim, Youngwon Kim, Miyoung Lee, Jung-Woo Oh, Hyon Park, Wook Song, Kyoungjune Yi, Yeon Soo Kim, Justin Y. Jeon

Bibliographic record

VenueJournal of Exercise Science & Fitness · 2023
Typeerratum
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of ManitobaUniversity of AlbertaQueen's University
Fundersnot available
KeywordsPhysical activityPandemicCoronavirus disease 2019 (COVID-19)Climate changeMedicinePhysical therapyInternal medicineGeologyOceanographyDisease

Abstract

fetched live from OpenAlex

[This corrects the article DOI: 10.1016/j.jesf.2022.10.014.].

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)0.036
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 score0.077
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.036
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.0770.046

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 head0.095
GPT teacher head0.371
Teacher spread0.276 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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