Virtual Reality and Tracking the Mating Behavior of Fruit Flies: a Machine Learning Approach
Bibliographic record
Abstract
Study of social behaviors of Drosophila melanogaster, i.e., fruit flies, is used to understand certain behaviors of human. Automation of capturing and real-time analysis of fruit fly social movements helps quantitative study of those behavior which in turns can be applied for studying human behavior. To achieve this, we present Virtual Reality environment setting to stimulate fruit flies behavior and tracking of their motions automatically in real-time. As an experiment in a real application, we selected study of mating behavior of fruit flies. Male fruit flies tend to extend their mating duration when exposed to rivals as published in previous biology studies. We designed a Virtual Reality environment where synthetic male fruit flies are virtually simulated to stimulate a male fruit fly to study the effect of rivals. Bezier curve fitting and Gaussian random distribution are utilized for movement simulation. A machine learning approaches (logistic regression) employed to track, detect, and classify fruit flies mating behavior. We performed connected component labeling as an operator for tracking and classification of mating and non-mating status for comparison purposes. The machine learning based approach shows superior result in terms of speed and accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".