Battery of Behavioral Tests Assessing General Locomotion, Muscular Strength, and Coordination in Mice
Bibliographic record
Abstract
Behavioral testing is used in pre-clinical trials to assess the phenotypic effects and outcomes that a particular disease or treatment has on the animal's wellbeing and health. There are numerous behavioral tests that may be applied. We selected a test for general locomotion, the open field test (OFT); a test for muscular strength, the mesh test (MT); and a test for coordination, the rotarod test (RR). Testing can be accomplished on a weekly or monthly basis. As a test for general locomotion, the OFT works by objectively monitoring movement parameters while the mouse is in an open field apparatus. The field is generally a 2' x 2' box, and the movements are recorded through laser sensing or through video capture. The mouse is placed in the center of the open field and allowed to move freely for the test. The MT uses the latency for a mouse to fall off an inverted screen as a measure of muscular strength. A mouse is placed on a screen, which is inverted over a clear box, and is timed for their latency to fall. Three trials are performed, with the best of the three trials scored for that day. A score of 60 s is the maximum time a mouse is left inverted. Mice are given a 5-min rest period between mesh test trials. Lastly, an accelerated protocol on the RR assesses motor coordination and endurance. During a trial, a mouse walks on a rotating rod as it increases in speed from 4 rpm to 40 rpm over 5 min. The trial ends when the mouse touches the magnetized pressure sensor upon falling. Each mouse undergoes three trials, and the best trial is scored for that day. This combined behavioral data allows for the global assessment of mobility, coordination, strength, and movement of the test animals. At least two out of the three behavioral testing measures must show improvement for an animal to qualify as having overall improved motor function.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".