Recreational angler reporting as a tool for tracking the distribution of invasive Prussian carp ( <i>Carassius gibelio</i> )
Bibliographic record
Abstract
Abstract The recent invasion of Carassius gibelio (commonly known as Prussian carp or Gibel carp) in freshwater environments in central Canada threatens native North American aquatic species and ecosystems. Accurate distribution information is essential for targeting control efforts but is challenging given the resources necessary to continually sample the species' potential distribution. We investigated the extent to which reports by recreational anglers—key resource users—could be used in a citizen science program to generate species distribution information, and factors affecting the accuracy of reporting for C. gibelio . Comparing the location of angler reports to the known distribution of C. gibelio generated by professional biological sampling across the region revealed that anglers can be a powerful resource for tracking an invasive species' distribution; 88% of the C. gibelio angler reports aligned with invaded watersheds (HUC‐8 [hydrological unit code 8], the second finest watershed unit) identified by professional biological sampling. For every report of C. gibelio received in a HUC‐8 area, the probability that area was invaded increased by more than 10 times (odds ratio = 10.26, ±95% CI: 4.4–29.7). Anglers' fish identification abilities were positively related to likelihood of reporting Carassius spp. (odds ratio = 2.52, ±95% CI:1.51–4.45). Anglers that fished more frequently were also more likely to have reported C. gibelio accurately (odds ratio = 1.00, ±95% CI: 0.99–1.01), although the mechanism behind this relationship is unclear. Our results suggest programs that engage recreational anglers in reporting could provide a cost‐effective alternative or complimentary tool for traditional Aquatic Invasive Species (AIS) population tracking.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".