An Open Source Automated Bar Test for Measuring Catalepsy in Rats
Bibliographic record
Abstract
Catalepsy bar tests are widely used to measure the failure to correct an imposed posture resulting from muscular rigidity. Procedures for measuring catalepsy vary greatly in the published literature, but one commonly used test measures the time it takes for a rodent to remove one or both of its forelimbs from a bar. The following paper describes an affordable, adjustable, open-source bar test that automatically measures and logs the time it takes for a rat to remove itself from a bar. While commercially available automated bar tests are prohibitively expensive, requiring proprietary software and hardware to operate, the proposed apparatus runs on an Arduino-based microcontroller making it low-cost and customizable. This 3D-printed design costs less than 65 United States dollars to build and is simple to assemble and operate. The beam-break sensor design also eliminates many of the pitfalls of the "complete-the-circuit"-based approach to recording catalepsy. The paper further describes the successful validation of the design using adult male rats injected with different doses of haloperidol to demonstrate a dose-dependent cataleptic effect. This design provides a versatile, low-cost solution to standardizing and automating measurement of catalepsy in rodents.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".