The influence of Wisconsinan glaciation and contemporary stream hydrology on microsatellite DNA variation in rainbow trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
Microsatellite DNA variation was examined in rainbow trout ( Oncorhynchus mykiss ) populations from throughout British Columbia, Canada, to address the roles of historical isolation, postglacial dispersal, and contemporary geomorphology in structuring genetic variation and differentiation. We detected signatures of historical isolation and postglacial recolonization in the form of “interior” and “coastal” population groupings, a decline in genetic variation as distance increased from putative glacial refugia, and different extents of isolation-by-distance in different regions. Rainbow trout populations were structured genetically into major regions and into smaller watersheds and then into drainages. Within drainages, high levels of dispersal and gene flow were inferred between geographically proximate and contiguous lakes. Elevation, stream branching points (nodes), fluvial distance, migration barriers, and stream and lake order influenced genetic diversity within, and differentiation among, populations. Habitat characteristics, particularly lake surface area and perimeter, were poor predictors of genetic variation. Although founder events and postglacial dispersal influenced broadscale patterns of genetic diversity in rainbow trout, our results suggest that contemporary factors can strongly modulate historical patterns.
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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 | 0.001 |
| 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".