MétaCan
Menu
Back to cohort
Record W2793080924 · doi:10.1080/15438627.2018.1431534

Direct player observation is needed to accurately quantify heading frequency in youth soccer

2018· article· en· W2793080924 on OpenAlexaff
Alexandra Harriss, David M. Walton, James P. Dickey

Bibliographic record

VenueResearch in Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsWestern University
Fundersnot available
KeywordsHeading (navigation)Computer scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

In soccer, heading may be related to subsequent neurological impairment. Accurate measures of heading exposure are therefore important. This study evaluated whether 12 female youth players accurately recalled their average number of headers over an entire soccer season (20 games total). Their self-reported average number of headers per game was multiplied by the number of games that they participated in, and were compared to actual number of headers extracted from game video. All players overestimated the number of headers compared to game video. Linear regression analysis indicated that self-reported headers overestimated the number of headers by 51%. While self-reports are a convenient way to estimate heading behaviour, they do not accurately represent the number of headers that players perform. Self-reports of heading exposure should be interpreted with caution.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.428
GPT teacher head0.493
Teacher spread0.065 · 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 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

Citations31
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueResearch in Sports MedicineSame topicSports Performance and TrainingFrench-language works237,207