Improving Widescale Monitoring of Ectoparasite Presence in Northern Canadian Wildlife with the Aid of Citizen Science
Bibliographic record
Abstract
Sampling hides from harvested animals is commonly used for passive monitoring of ectoparasites on wildlife hosts, but often relies heavily on community engagement to obtain spatially and temporally consistent samples. Surveillance of winter ticks (Dermacentor albipictus) on moose (Alces alces) and caribou (Rangifer tarandus caribou) hosts in Yukon, Canada, has relied in part on voluntary submission of hides by hunters since 2011, but few samples were submitted. To enhance sampling efforts on underrepresented moose and caribou hosts, we implemented a three-year citizen science program, the Yukon Winter Tick Monitoring Project (YWTMP), to better engage with hunters in hide sample collection. A combination of in-person and social media outreach, incentivized engagement, and standardized hide sampling kits increased voluntary submissions of moose and caribou hides almost 100-fold since surveillance began. Citizen science samples expanded the northernmost geographic extent of existing sampling efforts for moose by 480 km and for caribou by 650 km to reach 67.5° N latitude. Samples also resulted in new detections of winter ticks on moose hides that are spatially separate to those submitted for other cervids in Yukon. Findings from the YWTMP have provided an essential baseline to monitor future winter tick host–parasite dynamics in the region and highlighted priority areas for ongoing tick surveillance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".