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Record W2040833837 · doi:10.1089/jmf.2009.0223

Attenuation in Weight Gain with High Calcium- and Dairy-Enriched Diets Is Not Associated with Taste Aversion in Rats: A Comparison with Casein, Whey, and Soy

2010· article· en· W2040833837 on OpenAlexafffund
Lindsay K. Eller, Raylene A. Reimer

Bibliographic record

VenueJournal of Medicinal Food · 2010
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDairy Farmers of CanadaCanadian Diabetes Association
KeywordsFood scienceCaseinTasteCalciumWhey proteinSoy proteinWeight gainChemistryBody weightBiologyEndocrinology

Abstract

fetched live from OpenAlex

A systematic evaluation of the effects of calcium (Ca) and protein source on food intake and taste aversion (TA) in rats is lacking. The purpose of this research was twofold: (1) to determine if Sprague-Dawley rats display TA to standard rat chow supplemented with 2.4% Ca and (2) to determine if short (24-hour) and long-term (weekly) food intake and weight gain are altered when rats are given access to diets containing various protein sources (casein, whey, dairy, or soy). Rats were assigned to one of two diet groups to examine high (2.4%) versus low (0.67%) Ca or to one of four groups to examine taste preference of diets where the sole protein was one of casein, soy, whey, or complete dairy. A crossover design was used to ensure rats consumed all test diets. Food intake and behavioral sequence of satiety were measured. There was no TA to the 2.4% Ca diet or to any protein source. Food intake did not differ between the two Ca diets or between the four protein diets. The dairy diet attenuated weekly weight gain compared to all other diets except whey. Overall, this study suggests that the levels of Ca and types of protein used in previous work addressing changes in body weight in rats do not influence food intake or trigger TA.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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

Citations8
Published2010
Admission routes2
Has abstractyes

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