Population structure of lake whitefish (<i>Coregonus clupeaformis</i>) from the Mississippian lineage in North America
Bibliographic record
Abstract
The lake whitefish ( Coregonus clupeaformis) is a commercially valuable freshwater species with a broad distribution in North America. Some phylogeographic work has been done on this species, but little is known about genetic population subdivision among populations of the widely dispersed Mississippian lineage. We used 3,173 single nucleotide polymorphisms in 508 lake whitefish from 22 different lakes to examine population structure across central Canada and the United States. Bayesian clustering, ordination, and fixation indices identified population subdivision that largely reflected geographic distance and hydrological connectivity, with greater differentiation between lakes that are farther apart. Population subdivision was hierarchical, with greater differentiation between Canadian provinces and less differentiation based on river basins within provincial boundaries. Interestingly, isolation by distance alone was not sufficient to account for all of the observed genetic differentiation among populations. We conclude that important components of lake whitefish genetic diversity are present at different spatial scales, and that populations within the Mississippian lineage have differentiated widely across their range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".