Revealing migration and reproductive habitat of invasive fish under an active population suppression program
Bibliographic record
Abstract
Abstract Endemic species face a variety of threats including predation from non‐native invaders. In some cases, however, invasive species can be managed by directly suppressing populations, and tracking technologies that allow researchers to identify movement patterns and aggregations representative of the population can facilitate suppression activities. In Yellowstone Lake (Yellowstone National Park, Wyoming), invasive lake trout ( Salvelinus namaycush ) have been the target of a population suppression program for over two decades. For this form of management, the reproductive period is particularly important because fish migrate to and from spawning grounds. From 2011 to 2014, adult lake trout ( n = 317) in Yellowstone Lake were tracked using acoustic biotelemetry. After controlling for spatial and temporal dependency in the data, total abundance of unique individuals was estimated where migratory trajectories occurred at confirmed spawning sites. Aggregations and migratory trajectories were further estimated at locations where spawning had not previously been observed. Across years, the greatest number of individuals was observed along a migration corridor in the southwestern area of the lake. Novel strategies for analyzing acoustic telemetry data provided insights into the behavior of an invasive fish species. By betraying the positions of conspecifics, tagged fish revealed potentially important reproductive habitats and migration corridors that warranted further investigation as possible sites for population suppression.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".