The post-Wisconsinan glacial biogeography of bull trout (<i>Salvelinus confluentus</i>): a multivariate morphometric approach for conservation biology and management
Bibliographic record
Abstract
Canonical correlation analysis (CCA) can quantitatively partition historical and ecological information from morphometric data where these features are otherwise confounded. CCA is applied to sample site locality morphometric data and corresponding sample site locality coordinate data for bull trout. Two vectors result. The first accounts for the maximum morphometric variation correlated to geographic information specified by the locality coordinates. The second represents the remaining less correlated variation. For biogeography, the first vector generates historical hypotheses for Pleistocene glacial refugia and for post-Wisconsinan glacial recolonization patterns and phylogeographic relationships. The second vector infers hypotheses for broad ecological patterns. The historical biogeographic patterns for bull trout suggest recolonization from either two or three glacial refugia and emphasize within-species biodiversity in western North America. These patterns from the Chehalis and Columbia refugia are largely concordant with other analyses based on molecular genetics. The morphometric analysis also suggests the additional possibility of a Nahanni and (or) Bering refugium. The ecological patterns suggest the importance and extent of anadromy and migration within these historical groups and how this may have affected postglacial recolonization, present distributions, and life histories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".