Development and testing of a new system for assessing wheel-running behaviour in rodents
Bibliographic record
Abstract
BACKGROUND: Wheel running is one of the most widely studied behaviours in laboratory rodents. As a result, improved approaches for the objective monitoring and gathering of more detailed information is increasingly becoming important for evaluating rodent wheel-running behaviour. Here our aim was to develop a new quantitative wheel-running system that can be used for most typical wheel-running experimental protocols. FINDINGS: Here we devise a system that can provide a continuous waveform amenable to real-time integration with a high-speed video ideal for wheel-running experimental protocols. While quantification of wheel running behaviour has typically focused on the number of revolutions per unit time as an end point measure, the approach described here allows for more detailed information like wheel rotation fluidity, directionality, instantaneous velocity, and acceleration, in addition to total number of rotations, and the temporal pattern of wheel-running behaviour to be derived from a single trace. We further tested this system with a running-wheel behavioural paradigm that can be used for investigating the neuronal mechanisms of procedural learning and postural stability, and discuss other potentially useful applications. CONCLUSIONS: This system and its ability to evaluate multiple wheel-running parameters may become a useful tool for screening new potentially important therapeutic compounds related to many neurological conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".