MétaCan
Menu
Back to cohort
Record W2144877503 · doi:10.1080/02640414.2011.605459

Sport-specific nutrition: Practical strategies for team sports

2011· article· en· W2144877503 on OpenAlexaff
Francis E. Holway, Lawrence L. Spriet

Bibliographic record

VenueJournal of Sports Sciences · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAthletesCoachingAnthropometryTeam sportWork (physics)Sports nutritionSports medicinePsychologyPhysical therapyMedicineApplied psychologyEngineering

Abstract

fetched live from OpenAlex

Implementation of a nutrition programme for team sports involves application of scientific research together with the social skills necessary to work with a sports medicine and coaching staff. Both field and court team sports are characterized by intermittent activity requiring a heavy reliance on dietary carbohydrate sources to maintain and replenish glycogen. Energy and substrate demands are high during pre-season training and matches, and moderate during training in the competitive season. Dietary planning must include enough carbohydrate on a moderate energy budget, while also meeting protein needs. Strength and power team sports require muscle-building programmes that must be accompanied by adequate nutrition, and simple anthropometric measurements can help the nutrition practitioner monitor and assess body composition periodically. Use of a body mass scale and a urine specific gravity refractometer can help identify athletes prone to dehydration. Sports beverages and caffeine are the most common supplements, while opinion on the practical effectiveness of creatine is divided. Late-maturing adolescent athletes become concerned about gaining size and muscle, and assessment of maturity status can be carried out with anthropometric procedures. An overriding consideration is that an individual approach is needed to meet each athlete's nutritional needs.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.299
Teacher spread0.254 · 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 designNot applicable
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

Citations185
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sports SciencesSame topicMuscle metabolism and nutritionFrench-language works237,207