Getting a handle on rat familiarization: The impact of handling protocols on classic tests of stress in <i>Rattus norvegicus</i>
Bibliographic record
Abstract
Experimenter familiarization with laboratory rodents through handling prior to experimentation is an important practice in neurobehavioral research and is implicated in stress, study variability, and replicability. Unfortunately, different handling protocols have not been thoroughly examined. Determining optimal experimenter familiarization protocols is expected to reduce animal stress and thus improve welfare and data consistency. The impact of different handling protocols was determined through behavioral assessments (i.e. elevated plus maze, light/dark box, open field) as well as via analysis of fecal boli counts, ultrasonic vocalizations, and blood corticosterone. Male and female Sprague Dawley rats were distributed among three groups: never handled, picked-up, and handled for 5 min once daily over five days. Handled and picked-up rats spent more time in open arms and less time in closed arms of the elevated plus maze and more time in the center and less time at the perimeter of the open field compared to rats that were never handled, indicating that handled and picked-up rats were less anxious than those that were never handled. Male rats consistently defecated more frequently throughout the handling process and throughout behavioral testing, whereas females showed greater concentrations of blood corticosterone. Female rats were found to emit more 50-kHz calls and fewer 22-kHz calls compared to males. The results observed suggest that picking animals up may suffice as a handling method compared to time-intensive handling procedures, and that there are significant sex differences in response to handling.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".