An open source automated two-bottle choice test apparatus for rats
Bibliographic record
Abstract
Two-bottle choice tests are a widely used paradigm in rodents to determine preference between two liquids, with utility for testing animal models of addiction, depression and anhedonia. The following paper describes a 3D-printed, Arduino controlled two-bottle choice test that automatically reads and records drinking behavior in rats to allow for detailed analysis of their drinking microstructure. While commercial products exist use lickometers to measure the microstructure of licking, this design uniquely incorporates hydrostatic depth sensors to allow for real-time volumetric measurements in addition to traditional beam break lick sensing, allowing for licking and drinking microstructure analysis. The goal of this design is to provide a user friendly, affordable apparatus that can study unique, complex behaviors without requiring the purchase of specialized scientific equipment or software. Its applications range from studying alcohol preference in animal models of addiction to sucrose preference in motivational deficits and reward evaluation. This design costs less than $180 CAD to build with decreased cost on each additional device. This design has been successfully tested for accuracy and validated using alcohol preference as an example. The apparatus showed consistency between drinking bouts and volume consumed and is shown to be accurate to ±0.086 ml of the actual volume. This design makes using the two-bottle choice paradigm more accurate, while also making its data more robust and informative while allowing for microstructure analysis of both licking behavior and volume consumed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".