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Record W7099789435

Sport Nutrition Assessment: Questions, analysis and tools to consider

2015· article· en· W7099789435 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesSports nutritionEliteCommissionNutritionistElite athletesDeci-Sports medicine
DOInot available

Abstract

fetched live from OpenAlex

Gaining the right information is imperative to be a highly effective sport dietician- whether you are working with a newcomer or an Olympian. For a young or new athlete, a standard three-day dietary record, emphasizing nutritional basics and some recovery nutrition tips, will provide the majority of the support they may need. On the other side of the continuum, Sport Canada has identified the need for elite Olympic level athletes to develop an ‘Integrated Support Team ’ (IST) with their coaches- of which you, as a Registered Dietician (RD) or Nutritionist, may be apart of. An IST is a coach-driven group of professionals (doctors, physiotherapist, massage therapist, dietician/nutritionist, physiologist, sport psychologist, biomechanist etc.) who directly support and assist an athlete throughout the periodized yearly training program in their respective areas of expertise to allow the athlete to realize their fullest potential during a planned championship peak (Fig 1). Figure 1. Schematic representation of members and interaction of an IST With elite athletes, it is important that the level of support and expertise that a Registered Dietician (RD) / Nutritionist brings be developed and specialized. An appreciation of the individual athlete’s periodized training program and how nutrition can be periodized around that program need to be considered. Knowing general human physiology and metabolism will allow you to better assess the types of energy/fuels that athletes are using in training, and thus, be more in tune with the potential shift in macro and micronutrients that may need to occur to support their training and competition load. Dr. Powers and Howley have a great and easy-to-read textbook covering the basics in this area called Exercise Physiology: Theory and Application to Fitness and Performance – 7 th edition (Powers & Howley, 2008). Two other great textbook resources for any RD specializing in exercise and sport performance are both

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)0.014
metaresearch head score (Gemma)0.052
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.052
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.007

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 head0.082
GPT teacher head0.314
Teacher spread0.232 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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