Ecological Niche Modeling of <i>Cryptococcus gattii</i> in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Cryptococcus gattii emerged on Vancouver Island, British Columbia (BC), Canada, in 1999, causing human and animal illness. Environmental sampling for C.gattii in southwestern BC has isolated the fungal organism from native vegetation, soil, air, and water. OBJECTIVES: Our aim was to help public health officials in BC delineate where C.gattii is currently established and forecast areas that could support C.gattii in the future. We also examined the utility of ecological niche modeling (ENM) based on human and animal C.gattii disease surveillance data. METHODS: We performed ENM using the Genetic Algorithm for Rule-set Prediction (GARP) to predict the optimal and potential ecological niche areas of C.gattii in BC. Human and animal surveillance and environmental sampling data were used to build and test the models based on 15 predictor environmental data layers. RESULTS: ENM provided very accurate predictions (> 98% accuracy, p-value < 0.001) for C.gattii in BC. The models identified optimal C.gattii ecological niche areas along the central and south eastern coast of Vancouver Island and within the Vancouver Lower Mainland. Elevation, biogeoclimatic zone, and January temperature were good predictors for identifying the ecological niche of C.gattii in BC. CONCLUSIONS: The use of human and animal case data for ENM proved useful and effective in identifying the ecological niche of C.gattii in BC. These results are shared with public health to increase public and physician awareness of cryptococcal disease in regions at risk of environmental colonization of C.gattii.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".