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Record W2902012675 · doi:10.1111/obr.12783

Compensation in response to energy deficits induced by exercise or diet

2018· review· en· W2902012675 on OpenAlexaff
Éric Doucet, K. McInis, Salma Mahmoodianfard

Bibliographic record

VenueObesity Reviews · 2018
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsContext (archaeology)Compensation (psychology)Energy (signal processing)Weight lossEnergy expenditureEnergy balanceObesityMedicinePhysical medicine and rehabilitationPsychologyEndocrinologySocial psychologyBiologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Obesity is an extremely resilient condition. Weight loss is most challenging, and weight recidivism is rampant. There is accumulating evidence highlighting that energy deficits meant to produce increased mobilization of energy stores trigger a number of somewhat persistent adaptations that together increase the drive to eat and decrease energy output. These adaptations ostensibly enable a context where the likelihood of energy compensation is heightened. In fact, energy compensation is present for both diet and exercise induced energy deficits although at different magnitudes. For the most part, the energy compensation in response to exercise induced energy deficits seems to be larger. Interestingly, energy compensation appears to be greater for longer interventions, an effect independent of whether the energy deficit is induced through diet or exercise. The latter suggests that the increased drive to eat and the reduced energy expenditure that accompany weight loss might be successfully fought off initially. However, with time there seems to be increasing erosion of the behaviours that initially opposed adaptations to weight loss and increased energy compensation progressively sets in. Under such conditions, it would seem prudent to propose weight loss targets that align with a level of behaviour modifications that can be sustained indefinitely.

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)0.002
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.117
GPT teacher head0.401
Teacher spread0.284 · 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; both teacher heads agree on what is shown here.

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

Citations59
Published2018
Admission routes1
Has abstractyes

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