<scp>MHC</scp> Diversity Across Time and Space
Bibliographic record
Abstract
Most natural populations are genetically diverse. Understanding how diversity is maintained and distributed across time and space can provide insights into the potential for evolution and extinction of populations. Immunogenetic diversity aids individuals and populations in resisting infectious disease, with many studies linking resistance to genes encoding adaptive immune responses, such as the major histocompatibility complex (MHC) genes. The MHC is particularly important for advancing our understanding of local adaptive processes and host-parasite interactions. Here, we review the emerging work and theory exploring the geographic and temporal patterns of MHC diversity in the wild and how they are shaped by selective and demographic processes. We discuss patterns of variation along latitudinal and altitudinal gradients and place this in the context of Latitude Diversity Gradient and Central Marginal Theories. We emphasize how MHC diversity is often lower at the edges of species distributions, particularly in high-latitude and high-altitude regions. We also discuss MHC diversity in natural populations facing climate change. As climate change accelerates and emerging parasites spread, reduced immunogenetic diversity could severely threaten wildlife populations, compromising their resilience and long-term survival. We propose that including immunogenetic diversity into a larger database of environmental and parasite data would allow biologists to test hypotheses regarding host-parasite coevolution and develop effective measures for conservation.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".