A Case Study Leveraging Angler Reported Data For Whirling Disease Monitoring
Bibliographic record
Abstract
Abstract The Bow River (Alberta, Canada) has a well-documented decline in its rainbow trout fishery, with several stressors attributed to this decline including whirling disease (WD). This study evaluated whether anglers could detect recruitment failure in WD-impacted areas of the Bow River drainage using a smartphone app-based citizen science initiative. Anglers reported age 1 and 2 rainbow and cutthroat trout identified by size distribution, estimated from a provincial database, across 4 sub-watersheds with varying densities of Myxobolus cerebralis actinospores (TAMs), a known predictor of WD severity. TAM densities ranged from undetectable (0 TAMs/L - Waiparous Creek) to moderate (< 0.01 TAMs/L - Sheep and Highwood Rivers) and high (> 0.01 TAMs/L - Jumpingpound Creek). No fish were captured in Jumpingpound Creek over 20.2 angling hours. The Highwood and Sheep Rivers had catch rates of 0.4 and 0.2 age 1 and 2 trout per hour over 30.6 and 27.7 hours, respectively. Waiparous Creek, where WD was absent, had a significantly higher catch rate of 2.2 trout per hour over 20.3 hours. These findings suggest that self-reported angler data can potentially help identify recruitment losses in WD-impacted areas, demonstrating a novel application of citizen science in fisheries research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".