Rodent Tracking and Abnormal Behavior Classification in Live Video using Deep Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
This study is focused on developing tools to assess behavioral activity in neonatal mice undergoing opioid withdrawal. Opioids are used to manage pain in both young and old, but debilitating withdrawal symptoms can occur when opioid medications are stopped. We propose to apply various pre-trained neural network models such as ResNet, MobileNet, and EfficientNet to track mice behavior in live video using the DeepLabCut package. We track the animal by detecting movement and positioning of the “nose”, “right ear”, “left ear” and “tail base” in each video frame. Tracking is a prerequisite to detecting the abnormal behavior observed during precipitated withdrawal. We compare the performance of the different pretrained models in terms of accuracy. The highest accuracy that we achieved under abnormal conditions-opioid withdrawal using ResNet_50 was 95.82%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it