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Record W2162034691 · doi:10.3389/fnins.2013.00140

Circannual changes in stress and feeding hormones and their effect on food-seeking behaviors

2013· article· en· W2162034691 on OpenAlexaff
Shaina P. Cahill, Erin W. Noye Tuplin, Matthew R. Holahan

Bibliographic record

VenueFrontiers in Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsCarleton University
Fundersnot available
KeywordsGhrelinHormoneLeptinFood intakeEndocrine systemBiologyAnticipation (artificial intelligence)EndocrinologyInternal medicineObesityMedicine

Abstract

fetched live from OpenAlex

Seasonal fluctuations in food availability show a tight association with seasonal variations in body weight and food intake. Seasonal variations in food intake, energy storage, and expenditure appear to be a widespread phenomenon suggesting they may have evolved in anticipation for changing environmental demands. These cycles appear to be driven by changes in external daylength acting on neuroendocrine pathways. A number of neuroendocrine pathways, two of which are the endocrine mechanisms underlying feeding and stress, appear to show seasonal changes in both their circulating levels and reactivity. As such, variation in the level or reactivity to these hormones may be crucial factors in the control of seasonal variations in food-seeking behaviors. The present review examines the relationship between feeding behavior and seasonal changes in circulating hormones. We hypothesize that seasonal changes in circulating levels of glucocorticoids and the feeding-related hormones ghrelin and leptin contribute to seasonal fluctuations in feeding-related behaviors. This review will focus on the seasonal circulating levels of these hormones as well as sensitivity to these hormones in the modulation of food-seeking behaviors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.567

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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