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Record W4256310104 · doi:10.9734/afsj/2019/v8i229987

Fad Diet

2019· article· en· W4256310104 on OpenAlexaff
Mariam Omar, Faiza Nouh, Manal Younis, Moftah Younis, Nesma Nabil, Bushra Elamshity, Hajar Ahmad, Ibraheem Elhadad, Abdelraouf Elmagri

Bibliographic record

VenueAsian Food Science Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWeight lossCaloric theoryObesityType 2 diabetesCalorieMedicineEndocrinologyBody weightInternal medicineFood scienceDiabetes mellitusBiology

Abstract

fetched live from OpenAlex

This paper reviewed the common types of fad diets. Fad diets have an effective role in promoting weight loss, beneficial effects on body composition. Fad diets may protect against the development of obesity and related chronic diseases such as type two diabetes and coronary heart disease. Fad diets work simply because they restrict calorie intake, showing that the most important dietary concept of weight loss and maintenance is a decrease in caloric intake. Based on the contemporary studies on fad diets, the future concept for successful weight loss could run on the concept of energy density, which refers to the amount of energy in a given weight of food.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0360.013

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 head0.286
Teacher spread0.266 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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