Integration of Taste and Calorie Sensing in<i>Drosophila</i>
Bibliographic record
Abstract
Animals use gustatory information to assess the suitability of potential food sources and make critical decisions on what to consume. For example, the taste of sugar generally signals a potent dietary source of carbohydrates. However, the intensity of the sensory response to a particular sugar, or "sweetness," is not always a faithful reporter of its nutritional value, and recent evidence suggests that animals can sense the caloric content of food independently of taste. Here, we demonstrate that the vinegar fly Drosophila melanogaster uses both taste and calorie sensing to determine feeding choices, and that the relative contribution of each changes over time. Using the capillary feeder assay, we allowed flies to choose between sources of sugars that varied in their ratio of sweetness to caloric value. We found that flies initially consume sugars according to taste. However, over several hours their preference shifts toward the food source with higher caloric content. This behavioral shift occurs more rapidly following food deprivation and is modulated by cAMP and insulin signaling within neurons. Our results are consistent with the existence of a taste-independent calorie sensor in flies, and suggest that calorie-based reward modifies long-term feeding preferences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".