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Record W1968823391 · doi:10.1038/oby.2007.235

Overeating by Young Obesity‐prone and Lean Rats Caused by Tastes Associated With Low Energy Foods

2007· article· en· W1968823391 on OpenAlexafffund
W. David Pierce, C. Donald Heth, Joanna C. Owczarczyk, James C. Russell, Spencer D. Proctor

Bibliographic record

VenueObesity · 2007
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOvereatingMealObesityCaloric theoryEndocrinologyTasteFlavorInternal medicineFood scienceMedicineThirstJuvenileChemistryBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Childhood obesity is a prominent health problem that may involve early learning about tastes and the energy content of foods. We tested the hypothesis that food tastes predictive of low energy content cause overeating in young animals. RESEARCH METHODS AND PROCEDURES: Juvenile and adolescent (4- and 8-week-old) male JCR:LA-cp lean (+/cp or +/+) and obesity-prone (cp/cp) rats were given sweet (saccharin) and salty (sodium chloride) gelatin cubes made with starch (high caloric) or no starch (low caloric) for 16 days of taste conditioning. After 10 hours of food deprivation, rats received pre-meals with flavors that had been paired or unpaired with high caloric content during taste conditioning, followed immediately by measurement of chow intake at regular meals. RESULTS: Our findings show that both lean (+/cp) and obesity-prone (cp/cp) juvenile rats ate more regular chow after a pre-meal with a flavor associated with low caloric value than after a similar pre-meal with a flavor predictive of high caloric content. This effect occurred with juvenile rats but not with adolescents. DISCUSSION: Data from our study indicate that the subversion of the relationship between taste and caloric content disrupts the normal physiological and behavioral energy balance of juvenile rats, resulting in overeating that is independent of genetic disposition for obesity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.786

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 head0.006
GPT teacher head0.214
Teacher spread0.209 · 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 designBench or experimental
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

Citations25
Published2007
Admission routes2
Has abstractyes

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