Sickness behaviors across vertebrate taxa: proximate and ultimate mechanisms
Bibliographic record
Abstract
There is nothing like a pandemic to get the world thinking about how infectious diseases affect individual behavior. In this respect, sick animals can behave in ways that are dramatically different from healthy animals: altered social interactions and changes to patterns of eating and drinking are all hallmarks of sickness. As a result, behavioral changes associated with inflammatory responses (i.e. sickness behaviors) have important implications for disease spread by affecting contacts with others and with common resources, including water and/or sleeping sites. In this Review, we summarize the behavioral modifications, including changes to thermoregulatory behaviors, known to occur in vertebrates during infection, with an emphasis on non-mammalian taxa, which have historically received less attention. We then outline and discuss our current understanding of the changes in physiology associated with the production of these behaviors and highlight areas where more research is needed, including an exploration of individual and sex differences in the acute phase response and a greater understanding of the ecophysiological implications of sickness behaviors for disease at the population level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".