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Record W4361293401 · doi:10.1136/bmjsem-2023-001603

Sport and exercise medicine around the world: global challenges for a unique healthcare discipline

2023· editorial· en· W4361293401 on OpenAlexaff
Justin Carrard, Ana Morais Azevedo, Boris Gojanovic, Pascal Édouard, Tej Pandya, Diana Robinson, Gürhan Dönmez, Laila Ušacka, Rodrigo Alonso Martínez Stenger, Luciana De Michelis Mendonça, Jane S Thornton, Miguel Reis e Silva, Isabel Schneider, Johannes Zwerver, Moa Jederström, Kristina Fagher, Omar AlSeyrafi, Phathokuhle Cele Zondi, Fariz Ahamed, Mandy Zhang, Katja Van Oostveldt, Norasak Suvachittanont, Carole Akinyi Okoth, Loïc Bel, Eloise Matthews, Luke Nelson, Karen Kotila, Karsten Hollander, Patrick J. Owen, Evert Verhagen

Bibliographic record

VenueBMJ Open Sport & Exercise Medicine · 2023
Typeeditorial
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsWestern University
Fundersnot available
KeywordsSports medicineHealth careMedicinePsychologyPhysical therapyPolitical science

Abstract

fetched live from OpenAlex

of authors shown on this cover page is limited to 10 maximum.

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)0.015
metaresearch head score (Gemma)0.054
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.035
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.054
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0070.003
Science and technology studies0.0060.004
Scholarly communication0.0150.007
Open science0.0050.003
Research integrity0.0350.031
Insufficient payload (model declined to judge)0.0160.012

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 head0.060
GPT teacher head0.435
Teacher spread0.375 · 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
GenreEditorial

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

Citations9
Published2023
Admission routes1
Has abstractno

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