Automated Robotic Stimulation of Freely Moving Drosophila Larvae
Bibliographic record
Abstract
The Drosophila larva is an excellent model organism in biology for studying the mechanisms of sensory mechanotrans-duction and the neural basis of behavior. This paper reports an automated robotic system capable of force-controlled mechanical stimulation and locomotion behavior analysis of freely moving Drosophila larvae, which improves the force regulation accuracy and larva operation consistency over conventional manual manipulation. A 3D-printed, three-axis force sensor was developed and integrated into the robotic system to measure the contact force between the tip of an end-effector and the larva head, and an adaptive fuzzy proportional-integral-derivative (PID) controller was proposed for closed-loop control of the contact force. The three-axis force sensor was also employed to monitor lateral disturbances to the contact force caused by small larva head movements, and thus to validate the effectiveness of larva stimulation. The robotic system performed automated larva stimulation and locomotion analysis at a speed of four larvae per minute, and was applied to quantify the correlation between the applied contact force direction/magnitude and the larva reorientation behavior. With its high accuracy and efficiency, this system will greatly facilitate large-scale studies of mechanosensory behaviors in Drosophila larva.
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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".