Nonchemical Eradication of an Introduced Trout from a Headwater Complex in Banff National Park, Canada
Bibliographic record
Abstract
Abstract Nonnative salmonids have been intentionally introduced in North America for over a century and are now implicated in the decline of native aquatic species. Current management objectives for some national parks are to remove invasive species, where feasible. We evaluated the use of mechanical methods (gill nets and electrofishing) to eliminate a naturalized population of nonnative Brook Trout Salvelinus fontinalis in a subalpine headwater basin. We required a total of 1,383 continuous net nights/ha (net nights = number of nets × number of nights) over 5 years to completely eradicate 1,527 Brook Trout from both Lower (9.7 ha; 6 m maximum depth) and Middle (23.1 ha; 25 m maximum depth) Devon Lakes, Alberta, Canada. Electrofishing along the shoreline of both lakes resulted in the capture of 301 Brook Trout over 4 years. We required a total of 52.1 h/km of electrofishing over 5 years to completely remove 3,288 Brook Trout from the upper 4.5 km of the Clearwater River. We verified the absence of fish in the lakes with 1,558 net nights/ha of sampling over 4 years and in the river with 13.6 h/km over 2 years. This project confirms that mechanical methods are a viable option for removing introduced fish, even in relatively large (up to 20 ha) and deep (up to 25 m) mountain lakes. We surmised that our success (relative to that of projects described in the literature) was due to (1) the relatively simple alpine habitat, in which river and lake margins were clear of macrophytes, suspended sediment, and overhanging riparian vegetation, and (2) the sheer magnitude of our effort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".