A Risk Model for the Lyme Disease Vector Ixodes scapularis (Acari: Ixodidae) in the Prairie Provinces of Canada
Bibliographic record
Abstract
Lyme disease is emerging in Canada due to geographic range expansion of the tick vector Ixodes scapularis Say. Recent areas of emergence include parts of the southeastern Canadian Prairie region. We developed a map of potential risk areas for future I. scapularis establishment in the Canadian Prairie Provinces. Six I. scapularis risk algorithms were developed using different formulations of three indices for environmental suitability: temperature using annual cumulative degree-days > 0 °C (DD > 0 °C; obtained from Moderate Resolution Imaging Spectroradiometer satellite data as an index of conditions that allow I. scapularis to complete its life cycle), habitat as a combined geolayer of forest cover and agricultural land use, and rainfall. The relative performance of these risk algorithms was assessed using receiver-operating characteristic (ROC) area under the curve (AUC) analysis with data on presence-absence of I. scapularis obtained from recent field surveillance in the Prairie Provinces accumulated from a number of sources. The ROC AUC values for the risk algorithms were significantly different (P < 0.01). The algorithm with six categories of DD > 0 °C, habitat as a simple dichotomous variable of presence or absence of forest, and normalized rainfall had the highest AUC of 0.74, representing "fair to good" performance of the risk algorithm. This algorithm had good (>80%) sensitivity in predicting positive I. scapularis surveillance sites, but low (50%) specificity as expected in this region where not all environmentally suitable habitats are expected to be occupied. Further prospective studies are needed to validate and perhaps improve the risk algorithm.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".