Classification of social behavioral responses in stress and non-stress adult male mice with high precision
Bibliographic record
Abstract
Abstract To better characterize social behavior following stress exposure in mice, this study introduces a bidimensional analytical framework that extends beyond the limits of conventional analysis. By integrating a time-based social interaction ratio with the average distance to the CD1 aggressor, we propose a composite index that offers a more comprehensive assessment of social engagement during the social interaction test. This metric distinguishes between socially hesitant male and female mice— those entering the interaction zone while maintaining a relative distance from the CD1 aggressor—and mice that display robust sociability by both entering the zone and closely approaching the aggressor. By treating distance as a continuous variable, this approach moves beyond binary zone-based measures and enables a more refined phenotyping of individual differences along the resilience–susceptibility spectrum. This advancement is made possible using open-source, multipose-estimation tools such as DeepLabCut and DeepOF, which allow for high-resolution tracking and behavioral quantification. Our framework refines current preclinical models by capturing subtle behavioral adaptations to social stress. This ultimately improves their translational value for studying the neural and behavioral correlates of stress-related psychiatric disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".