Tracking Coaster Brook Trout to Their Sources: Combining Telemetry and Genetic Profiles to Determine Source Populations
Bibliographic record
Abstract
Abstract Radiotelemetry and genetic analysis are potent tools for informing fisheries management. Although they are usually applied independently, combining them can provide insights on fish origins, movement, and reproduction that could not otherwise be achieved. We applied these two techniques in two separate studies to resolve the origins and habitat use of coaster brook trout Salvelinus fontinalis in Nipigon Bay, Lake Superior. Telemetry of adult fish was used to determine the habitat use of coaster brook trout and microsatellite DNA genotyping to determine the relatedness of coasters to river-resident brook trout. Both studies indicated that coaster brook trout utilize multiple tributaries within Nipigon Bay for spawning. In terms of philopatric homing, however, the tracking data suggested that coasters show site fidelity between years, whereas the genetic data indicated substantial gene flow among tributaries. For individual tracked fish, the genetic data supported the homing hypothesis for 10 of 11 fish but also conclusively showed straying for 1 individual. The complementary insights from these combined data sets significantly clarify the behavior and habitat use of coaster brook trout and provide an example of the power of these combined methodologies for fisheries management.
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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 | 0.001 |
| 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) | 0.000 | 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".