Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station (INBEST)
Bibliographic record
Abstract
Due to rapid advances in genetic engineering, small rodents have become the preferred subjects in many disciplines of biomedical research. In studies of chronic CNS disorders, there is an increasing demand for murine models with high validity at the behavioral level. However, multiple pathogenic mechanisms and complex functional deficits often impose challenges to reliably measure and interpret behavior of chronically sick mice. Therefore, the assessment of peripheral pathology and a behavioral profile at several time points using a battery of tests are required. Video-tracking, behavioral spectroscopy, and remote acquisition of physiological measures are emerging technologies that allow for comprehensive, accurate, and unbiased behavioral analysis in a home-base-like setting. This report describes a refined phenotyping protocol, which includes a custom-made monitoring apparatus (Integrated Behavioral Station, INBEST) that focuses on prolonged measurements of basic functional outputs, such as spontaneous activity, food/water intake and motivated behavior in a relatively stress-free environment. Technical and conceptual improvements in INBEST design may further promote reproducibility and standardization of behavioral studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".