Data from: Impact of population expansion on genetic diversity and structure of river otters (Lontra canadensis) in central North America
Bibliographic record
Abstract
Populations of North American river otters (Lontra canadensis) declined throughout large portions of the continent during the early 1900s due to habitat degradation and unregulated trapping. River otters had been extirpated in North Dakota (ND), but the Red River Valley has since been recolonized, with potential source populations including the neighboring states of Minnesota or South Dakota, or the Canadian province of Manitoba (MB). We genotyped 9 microsatellite loci in 121 samples to determine the source population of river otters in the Red River Valley of ND, as well as to assess population structure and diversity of river otters in central North America. Overall, genetic diversity was high, with an average observed heterozygosity of 0.58. Genetic differentiation was low (F ST < 0.05) between river otters in ND and those of Minnesota, suggesting that eastern ND was recolonized by river otters from Minnesota. River otters from MB were genetically distinct from all other sampled populations. Low genetic differentiation (F ST = 0.044) between South Dakota and Louisiana (LA) suggested that reintroductions using LA stock were successful. The genetic distinctiveness of river otters from different geographic regions should be considered when deciding on source populations for future translocations.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".