PAW, a cost-effective and open-source alternative to commercial rodent running wheels
Bibliographic record
Abstract
Voluntary wheel running is a common measure of general activity in many rodent models across neuroscience and physiology. However, current commercial wheel monitoring systems can be cost-prohibitive to many investigators, with many of these systems requiring investments of thousands of dollars. In recent years, several open-source alternatives have been developed, and while these tools are much more cost effective than commercial system, they often lack the flexibility to be applied to a wide variety of projects. Here, we have developed PAW, a 3D Printable Arduino-based Wheel logger. PAW is wireless, fully self-contained, easy to assemble, and all components necessary for its production can be obtained for only $75 CAD. Furthermore, with its compact internal electronics, the 3D printed casing can be easily modified to be used with a wide variety of running wheel designs for a wide variety of rodent species. Data recorded with the PAW system shows circadian patterns of activity which is expected from mice and is consistent with results found in the literature. Altogether, PAW is a flexible, low-cost system that can be beneficial to a broad range of researchers who study rodent models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".