A machine learning framework for estimating the probability of blacklegged tick population establishment in eastern Canada using Earth observation data
Bibliographic record
Abstract
Ixodes scapularis ticks are the primary vector of Lyme disease (LD) in North America, and their range has expanded into southeastern and southcentral Canada with climate change. This study presents a comprehensive machine learning (ML) framework to estimate the probability of blacklegged tick population establishment as measured using active tick surveillance data. Environmental predictor variables were derived from Earth observation (EO) data at multiple spatial scales to assess their individual contributions in the prediction models. Among the tested ML algorithms, XGBoost emerged as the top-performing model, achieving high sensitivity (0.83) and specificity (0.71) in predicting population establishment. Performance was optimized when using predictor variables derived from a 1 km radius around surveillance sites. Top predictors included cumulative annual degree-days above 0°C and maximum temperature of warmest month, reflecting the importance of temperature in enabling tick survival and reproduction. Additional predictor variables of high importance included silty soil (lower clay content) with slightly higher than average SOC and pH, and land cover types that contained broadleaf forests (percent mixed forest, percent broadleaf) and less urban areas. By integrating ML with open access EO data, this study demonstrates that accurate, easily updatable risk maps can be produced to support public health management of LD, and more broadly, the growing threat of tick-borne diseases in a changing climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".