The CApillary FEeder Assay Measures Food Intake in <em>Drosophila melanogaster</em>
Bibliographic record
Abstract
For most animals, feeding is an essential behavior for securing survival, and it influences development, locomotion, health and reproduction. Ingestion of the right type and quantity of food therefore has a major influence on quality of life. Research on feeding behavior focuses on the underlying processes that ensure actual feeding and unravels the role of factors regulating internal energy homeostasis and the neuronal bases of decision-making. The model organism Drosophila melanogaster, with its great variety of genetically traceable tools for labeling and manipulating single neurons, allows mapping of neuronal networks and identification of molecular signaling cascades involved in the regulation of food intake. This report demonstrates the CApillary FEeder assay (CAFE) and shows how to measure food intake in a group of flies for time spans ranging from hours to days. This easy-to-use assay consists of glass capillaries filled with liquid food that flies can freely access and feed on. Food consumption in the assay is accurately determined using simple measurement tools. Herein we describe step-by-step the method from setup to successful execution of the CAFE assay, and provide practical examples to analyze the food intake of a group of flies under controlled conditions. The reader is guided through possible limitations of the assay, and advantages and disadvantages of the method compared to other feeding assays in D. melanogaster are evaluated.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".