Infection state can affect host migratory decisions
Bibliographic record
Abstract
Organisms across a wide range of taxa use migration as a strategy to avoid, reduce or recover from parasitic infection. Previous work has identified three different processes by which migration can help reduce infection risk and/or costs: migratory escape from infection, migratory culling of infected individuals and migratory recovery from infection. However, most theoretical modelling of host migration in response to infection assumes that individuals have a single strategy during both infected and susceptible states, meaning an individual's state (susceptible or infected) is irrelevant to its decision to migrate. Here, we construct a model with two independent strategies of migration for an individual based on whether they are infected or susceptible, to study when the decision to migrate is favourable. We show that the best strategy for individuals of a given infection state is to either always migrate or never migrate. When infected and susceptible individuals differ in their migration strategy, this leads to partial migration at the population level (only some individuals migrate). Whereas previous theory showed that partial migration can be optimal in the face of infection, our work further parses out the contributors to partial or complete migration by determining which individuals in the population are involved in migration for different sets of conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".