Commentary: Best practices for performing olfactory behavioral assays on aquatic animals: A guide for comparative physiologists
Bibliographic record
Abstract
As more physiologists start to incorporate animal behavior into their experiments, especially in the olfactory behavior research field, some considerations are often overlooked, partly due to the inherited way that physiological experiments are traditionally designed and performed. Here we highlight some of these subtle but important considerations and make a case for why these might affect the results collected from behavioral assays. Our aim is to provide useful suggestions for increased standardization of methods so they can be more easily replicated among different experiments and laboratories. We have focused on areas that are less likely to be mentioned in the materials and methods section of a manuscript such as starvation, preliminary experiments, appropriate sample sizes and considerations when choosing an odorant for an assay. Additionally, we are strongly cautioning against the use of alarm cue to generate behavioral responses due to its highly unstable chemical properties/potency. Instead, we suggest using pure chemicals (made up of one known molecule) such as amino acids, bile acids, or polyamines that are commercially available and easier to make up in known concentrations. Lastly, we strongly suggest using environmentally relevant concentrations of these odorants. We believe these guidelines will help standardize these assays and improve replication of experiments within and between laboratories. • We provide considerations when performing olfactory behavioral tests. • These include using appropriate samples sizes, pure chemicals as odorants at environmentally relevant concentrations. • Avoid starving animals when testing predator odorants and avoid using alarm cue. • Use appropriate lighting and avoid air exposure of the animal when transferring from exposure tank to the behavioral arena. • These are aimed at increasing reproducibility of results in this research field.
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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 | 0.018 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.061 | 0.073 |
| Insufficient payload (model declined to judge) | 0.013 | 0.027 |
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".