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Record W2148252025 · doi:10.1210/jc.2003-030780

Water-Induced Thermogenesis

2003· article· en· W2148252025 on OpenAlexaff
Michael Boschmann, Jochen Steiniger, Uta Hille, Jens Tank, Frauke Adams, Arya M. Sharma, Susanne Klaus, Friedrich C. Luft, Jens Jordan

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2003
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcMaster UniversityHamilton General Hospital
Fundersnot available
KeywordsThermogenesisEnergy expenditureEnergy metabolismCalorimetryWeight lossMetabolic rateAnimal scienceSpecific dynamic actionBasal metabolic rateChemistryMicrodialysisAdipose tissueDoubly labeled waterBrown adipose tissueEndocrinologyInternal medicineMedicineBiologyObesity

Abstract

fetched live from OpenAlex

Drinking lots of water is commonly espoused in weight loss regimens and is regarded as healthy; however, few systematic studies address this notion. In 14 healthy, normal-weight subjects (seven men and seven women), we assessed the effect of drinking 500 ml of water on energy expenditure and substrate oxidation rates by using whole-room indirect calorimetry. The effect of water drinking on adipose tissue metabolism was assessed with the microdialysis technique. Drinking 500 ml of water increased metabolic rate by 30%. The increase occurred within 10 min and reached a maximum after 30-40 min. The total thermogenic response was about 100 kJ. About 40% of the thermogenic effect originated from warming the water from 22 to 37 C. In men, lipids mainly fueled the increase in metabolic rate. In contrast, in women carbohydrates were mainly used as the energy source. The increase in energy expenditure with water was diminished with systemic beta-adrenoreceptor blockade. Thus, drinking 2 liters of water per day would augment energy expenditure by approximately 400 kJ. Therefore, the thermogenic effect of water should be considered when estimating energy expenditure, particularly during weight loss programs.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.387
Teacher spread0.297 · 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 designNot applicable
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

Citations152
Published2003
Admission routes1
Has abstractyes

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