MétaCan
Menu
Back to cohort
Record W2423662161 · doi:10.1123/ijspp.1.2.169

Technical Issues in Quantifying Low-Frequency Fatigue in Athletes

2006· article· en· W2423662161 on OpenAlex
Jonathon R. Fowles

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Sports Physiology and Performance · 2006
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsAcadia University
Fundersnot available
KeywordsAthletesPhysical medicine and rehabilitationMedicinePhysical therapy

Abstract

fetched live from OpenAlex

A recent review by Cairns and colleagues published in Exercise and Sport Sciences Reviews (2005:33[1]:9-16)1 described experimental models used to study neuromuscular fatigue and explained the inherent strengths and weaknesses of applied versus reductionist approaches. This technical report addresses some of the recommendations made in that review, from the perspective of the applied sport scientist or practitioner in evaluating fatigue in elite athletes. The goal here is to highlight the inherent difficulties in assessing fatigue in the applied sport setting and to provide practitioners with future directions for fatigue research. A particular type of fatigue, called low-frequency fatigue (LFF), is of particular interest to the applied sport scientist or practitioner and could be the focus of future work. This report identifies some of the technical challenges faced in developing a practical test of LFF for use in the field setting. The outcome of further work in this area will lead to a better understanding of athlete monitoring, training, and performance.

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.

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)0.000
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 score0.180
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.336
Teacher spread0.308 · 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