The Odor Delivery Optimization Research System (ODORS): An Open-Source Olfactometer for Behavioral Assessments in Tethered and Untethered Rodents
Bibliographic record
Abstract
Olfaction is the dominant sensory modality in rodents. It can be used to assess behavioral phenomena including stress, learning and memory, and social investigation, and impaired olfaction is implicated in several neurological disorders. Paradigms such as the olfactory habituation/dishabituation (OHD) task can assess olfactory perception, memory, and motivation. However, these tasks require manual stimulus presentation, introducing variability and making them labor-intensive. Olfactometers allow automated stimulus delivery, but the OHD task has not yet been adapted for use with an olfactometer. Additionally, current olfactometer designs require proprietary software or components that are difficult to obtain/fabricate and commercial units are expensive. As a result, these apparatuses have not been widely implemented. Here, we describe the design and assembly of the Odor Delivery Optimization Research System (ODORS), an economical, modular, and open-source olfactometer for use in rodents, and describe a variant of the OHD task that can be automated using this apparatus. The design is based on five principles: (1) familiar layout and function; (2) use of inexpensive, readily available components; (3) easily integrated, modular design; (4) real-time assessment of odorant levels; and (5) the ability to test tethered and untethered rodents in optogenetic and electrophysiological experiments. Male and female C57BL/6NCrl mice performing OHD in the ODORS exhibit the characteristic habituation to repeated presentations of an odor and dishabituation to the first presentation of a novel odor. As a result, we suggest that the ODORS makes improved olfactory testing accessible to many labs and offers a major refinement over existing OHD testing paradigms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".