The negative relationship between mammal host diversity and Lyme disease incidence strengthens through time
Bibliographic record
Abstract
Since its discovery in 1975, Lyme disease has spread and increased in much of central and eastern United States. Host diversity is thought to play a role in Lyme disease risk, and it has been suggested that the direction of the relationship between host diversity and disease risk may vary depending on the spatial scale of observation. Here we modelled the effect of mammal host species richness on the incidence of Lyme disease from 1992 to 2011 across all states in the United States with reported or established black‐legged tick (Ixodes scapularis) populations. We tested two contrasting hypotheses: a positive vs. a negative relationship between host species richness and Lyme disease incidence. We also tested the hypothesis that the strength of the diversity–disease‐risk relationship increased over time, as Lyme disease spread. We observed a strong negative relationship between mammal host species richness and Lyme disease incidence, and this relationship became more negative over time. Lyme disease increased over time more rapidly in host species‐poor states than host species‐rich states. Our findings support the importance of mammal host richness on Lyme disease risk at large spatial scales, and the importance of spatial and temporal scales on the diversity–disease relationship.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".