Stream trees: a statistical method for mapping genetic differences between populations of freshwater organisms to the sections of streams that connect them
Bibliographic record
Abstract
Statistical approaches for studying the spatial distribution of genetic diversity that assume that organisms move through a two-dimensional landscape are not well suited to study populations of freshwater fish. We present a new statistical method for mapping genetic differences among populations of freshwater fish to the sections of streams that connect them. The method is useful for freshwater species that can only disperse through stream corridors and for other species that live in habitats for which there is one, and only one, corridor connecting each pair of populations (e.g., alpine organisms confined to ridge tops). The model is a simple extension of the least-squares method for constructing evolutionary trees. In this model, the genetic distances between populations are modeled as a sum of genetic distances mapped onto landscape features (e.g., stream sections). Analysis of simulated data shows that the method produces useful results with realistic amounts of data. The model was fit to empirical microsatellite data from four metapopulations of freshwater fish and showed an excellent fit in three out of four cases. Software to perform the necessary calculations is available from the authors at www.montana.edu/kalinowski .
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".