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Record W2106351115 · doi:10.1139/h05-022

Dietary Reference Intakes for the macronutrients and energy: considerations for physical activity

2006· article· en· W2106351115 on OpenAlexaffvenue
Gordon A. Zello

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDietary Reference IntakeNutrientEnvironmental healthPhysical activityReference Daily IntakeHuman nutritionDietary fiberEssential nutrientMedicineFood scienceGerontologyChemistryPhysical therapy

Abstract

fetched live from OpenAlex

The Dietary Reference Intakes (DRIs) are the North American reference standards for nutrients in the diets of healthy individuals. The macronutrient DRI report includes the standards for energy, fat and fatty acids, carbohydrate and fiber, and protein and amino acids. Equations used to identify the Estimated Energy Requirement (EER) were also developed based on individual characteristics including levels of physical activity. The DRIs for the macronutrients are presented as Recommended Dietary Allowances (RDAs) or Adequate Intakes (AIs), as well as Acceptable Macronutrient Distribution Ranges (AMDRs), and were arrived at by considering both nutrient inadequacies and excesses. In addition, recommendations are made that would reduce the risk of chronic diseases, such as setting intake limits for added sugar; reducing cholesterol, saturated, and trans fatty acids consumption; and increasing levels of physical activity. As healthy individuals include those engaged in various levels of physical activity, the DRIs should apply to the athlete and address their macronutrient and energy needs. This paper summarizes the macronutrient DRI report as applied to the adult, with discussion of the dietary needs of those engaged in various levels of physical activity, including the athlete.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.505

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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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 designBench or experimental
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

Citations68
Published2006
Admission routes2
Has abstractyes

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