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Resistance To Weight Gain During Overfeeding: A Neat Explanation

2009· review· en· W1987037561 on OpenAlexaff
Theodore B. Vanltallie

Bibliographic record

VenueNutrition Reviews · 2009
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsColumbia College
Fundersnot available
KeywordsWeight gainResistance (ecology)PsychologyMedicineEndocrinologyBiologyBody weightAgronomy

Abstract

fetched live from OpenAlex

Individuals vary in susceptibility to weight gain in response to overeating; however, the reason for such variation has never been clear. A recent study of 16 nonobese young adults followed on an ambulatory basis for 8 weeks found that changes in nonexercise activity thermogenesis (NEAT) account for the variations in fat storage that occur in response to experimentally controlled overeating. NEAT is the thermogenesis that accompanies physical activity other than volitional exercise. Individuals in whom overeating effectively activates NEAT dissipate as much as 69% of the excess energy as heat. Those less able to activate NEAT store a higher proportion of the excess calories as fat. Other studies have shown that genotype is an important determinant of resistance to overfeeding-induced weight gain. Spontaneous weight gain is accompanied by rises in plasma norepinephrine, insulin, and leptin levels, suggesting that a change in autonomic nervous system activity or in pattern of hormonal secretion might play a role in the activation of overeating-induced NEAT

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.062
GPT teacher head0.352
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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2009
Admission routes1
Has abstractyes

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