A multi-scale investigation of the relationship between host diversity and Lyme disease
Bibliographic record
Abstract
This thesis investigates the relationship between host diversity and Lyme disease. There are two main hypotheses linking host diversity to Lyme disease incidence; the first, the "dilution effect" hypothesis predicts a negative relationship, while the second, the "amplification effect" hypothesis predicts a positive relationship. Research to date suggests that the diversity-Lyme disease relationship is more nuanced than either of these hypotheses, such that the observed relationship may be dependent upon the particular temporal or spatial scale of observation, among other factors. In my first chapter, I review previous theory, models, and empirical evidence relating to the host diversity-Lyme disease relationship. Chapter 2 is a statistical investigation, using generalized linear mixed models, of the relationship between tick host species richness and Lyme disease incidence in the United States from 1992 to 2011. We found an increasingly negative relationship between host diversity and disease incidence in time, indicating an increasing dilution effect. Chapter 3 is a statistical analysis, again using a generalized linear mixed model, of the relationship between small mammal diversity and the density of Borrelia burgdorferi infection, the pathogen responsible for Lyme disease in 27 forest sites in Southern Quebec in 2011, 2012, and 2013. We found a positive relationship between small mammal diversity and the density of Borrelia burgdorferi, indicating an amplification effect. We then explored the mechanisms driving this diversity-disease relationship in Southern Quebec using structural equation models. The contrasting findings of these two studies, which take place at different spatial and temporal scales, as well as at different degrees of Lyme disease emergence, reinforce previous findings that the diversity-Lyme disease relationship is highly context-dependent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".