Bibliographic record
Abstract
There are well established links between excessive commuting and negative health consequences for humans. From a physiological perspective, commuting increases BMI and blood pressure while decreasing cardiorespiratory fitness and physical activity. From a psychological perspective, commuting increases sleep disorders and psychological stress. These negative consequences are also observed in animals. In addition to the adverse effects on the wellbeing of research animals, the impact of transportation threatens the scientific accuracy of our models. The intensive development, use and sharing of diverse rodent models in scientific studies, requires ongoing and at times global shipment of many animals. This transportation heavily impacts rodent physiology, including cardiovascular, endocrine, immune, and digestive systems. These all lead to changes in their neurophysiology and ultimately, behavior. If not accounted for, these practices will introduce confounding variables with the potential to conflate most studies. This review summarizes the research from the last 20 years in this area. Specifically, the effects of transportation on rodent physiology and behavior are reviewed. Through meta-analysis, these changes are quantified and analysed. Lastly, some recommendations for optimizing rodent transportation are discussed, to reduce the adverse effects of shipping and provide a means for mitigating this key but underappreciated aspect of contemporary animal research. Canada Research Chair Program and NSERC This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".