Stress exposure, food intake and emotional state.
Bibliographic record
Abstract
This manuscript summarizes the proceedings of the symposium entitled, "Stress, Palatable Food and Reward", that was chaired by Drs. Linda Rinaman and Yvonne Ulrich-Lai at the 2014 Neurobiology of Stress Workshop held in Cincinnati, OH. This symposium comprised research presentations by four neuroscientists whose work focuses on the biological bases for complex interactions among stress, food intake and emotion. First, Dr Ulrich-Lai describes her rodent research exploring mechanisms by which the rewarding properties of sweet palatable foods confer stress relief. Second, Dr Stephanie Fulton discusses her work in which excessive, long-term intake of dietary lipids, as well as their subsequent withdrawal, promotes stress-related outcomes in mice. Third, Dr Mark Wilson describes his group's research examining the effects of social hierarchy-related stress on food intake and diet choice in group-housed female rhesus macaques, and compared the data from monkeys to results obtained in analogous work using rodents. Finally, Dr Gorica Petrovich discusses her research program that is aimed at defining cortical-amygdalar-hypothalamic circuitry responsible for curbing food intake during emotional threat (i.e. fear anticipation) in rats. Their collective results reveal the complexity of physiological and behavioral interactions that link stress, food intake and emotional state, and suggest new avenues of research to probe the impact of genetic, metabolic, social, experiential and environmental factors on these interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".