Evaluating the submission of digital images as a method of surveillance for <i>Ixodes scapularis</i> ticks
Bibliographic record
Abstract
Widespread access to the internet is offering new possibilities for data collection in surveillance. We explore, in this study, the possibility of using an electronic tool to monitor occurrence of the tick vector of Lyme disease, Ixodes scapularis. The study aimed to compare the capacity for ticks to be identified in web-based submissions of digital images/photographs, to the traditional specimen-based identification method used by the provincial public health laboratory in Quebec, Canada. Forty-one veterinary clinics participated in the study by submitting digital images of ticks collected from pets via a website for image-based identification by an entomologist. The tick specimens were then sent to the provincial public health laboratory to be identified by the 'gold standard' method using a microscope. Of the images submitted online, 74·3% (284/382) were considered of high-enough quality to allow identification. The laboratory identified 382 tick specimens from seven different species, with I. scapularis representing 76% of the total submissions. Of the 284 ticks suitable for image-based species identification, 276 (97·2%) were correctly identified (Kappa statistic of 0·92, Z = 15·46, P < 0·001). This study demonstrates that image-based tick identification may be an accurate and useful method of detecting ticks for surveillance when images are of suitable quality.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".