Use of genetic markers to aid in re-establishing migratory connectivity in a fragmented metapopulation of bull trout (<i>Salvelinus confluentus</i>)
Bibliographic record
Abstract
Dams and other barriers fragment important migratory corridors for bull trout ( Salvelinus confluentus ) across the species range. Three dams constructed without fish passage facilities prevented migratory bull trout in the Lake Pend Oreille and Clark Fork River system in Idaho and Montana, USA, from returning to their natal spawning tributaries for nearly 100 years. We genotyped bull trout from 39 spawning tributaries to assemble a baseline data set that we used to develop a real-time genotyping and analysis protocol to assist with upstream fish transport decisions. Self-assignment tests and analysis of blind samples indicated that unknown individuals could be assigned to their region of origin with a high degree of confidence. From 2004 to 2010, genetic assignments were conducted for 259 adult bull trout collected below mainstem dams. Based on genetic assignments, 203 fish were transported upstream above one or more dams. This protocol has helped re-establish connectivity in a fragmented system, providing increased numbers of spawning adults for numerically depressed populations above the dams. We discuss the utility of genetic data for assisting with upstream passage decisions.
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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 | 0.002 |
| 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 | 0.000 | 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".