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Record W3203195241

FAD DIETARY PATTERNS AND THEIR IMPACT ON HUMAN HEALTH

2020· article· en· W3203195241 on OpenAlexvenueno aff
Aiza Yasin, Muhammad Anees Ur Rehman

Bibliographic record

VenueAdvanced Food and Nutritional Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsWeight lossVegan DietMedicineFood scienceObesityBiologyEndocrinologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Adiet, popular for a time, without standarddietaryrecommendations, and promising unreasonably fast weight loss or nonsensical health improvements is called Fad diet. These diets involve the elimination of foods that contain essential nutrients, some diets also cut entire food groups. The most common fad diets include Detox Diet, General Motor Diet, South Beach Diet, Hollywood Diet, Banana Diet, Paleo Diet, Raw Food Diet, Keto Diet, Vegan Diet, Atkins Diet, Dubrow Diet, Watermelon Diet, Blood Type Diet, Alkaline Diet and Liquid Diet. All the diets result drastic and unrealistic weight loss. These unhealthy dietary patterns make unrealistic promises but do not result in long-term weight loss. It may severely jeopardize health of a human being. The fact that these diets they don't work and also provide more suffering in your efforts to lose weight. The consequences of these diets are inadequate intake of minerals and vitamins, low caloric intake, fatigue, weakness, dehydration, constipation, gastrointestinal discomfort, bad breath, disrupt metabolic rate etc. The best approach to lose weight is to consult a qualified dietitian/nutritionist who'll asses' dietary intake and recommend a healthy eating long-term weight loss plan according to the body 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.233

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 head0.070
GPT teacher head0.360
Teacher spread0.290 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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