Tickling, a Technique for Inducing Positive Affect When Handling Rats
Bibliographic record
Abstract
Handling small animals such as rats can lead to several adverse effects. These include the fear of humans, resistance to handling, increased injury risk for both the animals and the hands of their handlers, decreased animal welfare, and less valid research data. To minimize negative effects on experimental results and human-animal relationships, research animals are often habituated to being handled. However, the methods of habituation are highly variable and often of limited effectiveness. More potently, it is possible for humans to mimic aspects of the animals' playful rough-and-tumble behavior during handling. When applied to laboratory rats in a systematic manner, this playful handling, referred to as tickling, consistently gives rise to positive behavioral responses. This article provides a detailed description of a standardized rat tickling technique. This method can contribute to future investigations into positive affective states in animals, make it easier to handle rats for common husbandry activities such as cage changing or medical/research procedures such as injection, and be implemented as a source of social enrichment. It is concluded that this method can be used to efficiently and practicably reduce rats' fearfulness of humans and improve their welfare, as well as reliably model positive affective states.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".