Bibliographic record
Abstract
Many of the standard predictions in evolutionary epidemiology result from models in which all hosts are equally susceptible to acquiring an infection and equally capable of resisting pathogens once an infection has been established. This contrasts with the empirical reality that natural host populations are typically composed of individuals with various susceptibilities and vulnerabilities to pathogen exploitation that can influence all aspects of a given pathogen's transmission-virulence phenotype. In these structured host settings, host-dependent variation in the virulence-transmission trade-off plays an important role in determining pathogen evolution. By deriving some game-theoretic equilibrium expressions that describe pathogen evolution in heterogeneous host populations, the contribution of host heterogeneity to the direction of evolution in host exploitation is made explicit. Within this framework, qualitative departures from predictions derived from theory utilizing a homogeneous host assumption can be seen as a manifestation of Simpson's paradox in an evolutionary setting. By reconsidering some predictions from homogeneous host theory through the lens of this new perspective, it can be seen that many standard predictions are actually special cases that result when homogeneity in immunity parameters is imposed on host populations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".