Leveraging omics for bull trout conservation
Bibliographic record
Abstract
Declines in bull trout ( Salvelinus confluentus) populations across North America have prompted increased collaboration among academic, governmental, private, nonprofit, and Indigenous groups to improve conservation strategies. Bull trout experience multiple stressors including habitat fragmentation, interspecific competition, fishing mortality, and climate change-induced warming. These threats to bull trout populations highlight the need to utilize approaches to rapidly assess the health and status of wild populations. This review synthesizes recent advances in omics tools applied to bull trout conservation with an emphasis on transcriptional profiling, genomics, and environmental DNA. Given their protected status, which limits lethal sampling in the wild, nonlethal and minimally invasive sample collection is required. Integrating omics with existing frameworks such as species distribution modeling has the potential to modernize conservation practices and guide recovery strategies for this at-risk species. Addressing limitations of current omics approaches for bull trout, including the absence of a sequenced genome, will be important to further advance tools for their 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".