Forest Disturbance and Disease: Exploring the Effects of Tree Harvesting Area on <i>Cryptococcus gattii</i> sensu lato Infection Risk, Vancouver Island, Canada, 1998–2014
Bibliographic record
Abstract
Background: The disturbance of colonized trees and soil, such as through forestry activities, has been proposed to disperse soil- and tree-inhabiting fungal pathogens. Cryptococcus gattii sensu lato is one such pathogen that was detected on Vancouver Island, British Columbia, Canada, beginning in 1999 and caused human and animal illness. Objectives: Our aim was to determine if C. gattii s.l. human case incidence on Vancouver Island was correlated with the intensity of landscape-level tree harvesting occurring near human settlement areas. Methods: We created buffers around human settlement areas with radii increments of 2.5km, from 2.5 to 20km, and summed the area of annual tree harvests occurring within each buffer zone. We then performed Spearman rank–order correlation to measure the association between case incidence and annual tree harvest intensity at each radius from 1998 through 2014. Results: The incidence of C. gattii was positively correlated with tree harvesting intensity only at distances of 7.5km (r=0.66, p=0.004) and 10km (r=0.64, p=0.005) from human settlement areas. As annual tree harvesting area increased between 1999 and 2003, so did annual C. gattii incidence in humans, before both plateaued around 2002 and decreased after 2007. Discussion: Our findings suggest that tree harvesting plays a role in the spread of C. gattii on Vancouver Island. This may be due to tree cutting or soil disturbance facilitating the aerosolization of spores to increase infection risk. This research also illustrates the contribution that geographic information systems can make to public health research on environmental disturbance and disease outbreaks. https://doi.org/10.1289/EHP12396
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".