Genetic monitoring of trout movement after culvert remediation: family matters
Bibliographic record
Abstract
We contrasted various genetic analyses to evaluate their utility and constraints for detecting movement of cutthroat trout (Oncorhynchus clarkii) through restored culverts in different field settings: population-level metrics of genetic variability (heterozygosity and allelic richness); Bayesian clustering and assignment of individual genotypes from age 1+ fish; and a novel “sib-split” approach, where movement patterns are extracted from the spatial distribution of young-of-year (YOY) full-sibling groups inferred via pedigree reconstruction. Family structure greatly influenced population-level and individual clustering results in our small headwater populations, even though field sampling was implemented to avoid siblings. Sib-split, which uses family structure to detect movement, uncovered passage of YOY just weeks after emergence. When retrospectively applied to older individuals, it proved essential in interpreting clustering patterns and captured passage in several families of 1- and 2-year-olds. Where family structuring may negatively affect genetic analyses or, alternatively, be prominent enough to allow application of sib-split is difficult to predict a priori; we discuss benefits and limitations of all approaches under different ecological, spatial, and management scenarios.
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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 | 0.001 | 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".