Population subdivision and peripheral isolation in American badgers (<i>Taxidea taxus</i>) and implications for conservation planning in Canada
Bibliographic record
Abstract
In Canada, three subspecies of American badgers ( Taxidea taxus (Schreber, 1777)) traditionally are identified; two of which are listed as endangered because of their restricted geographic range and low population sizes. To verify their subspecific designations and genetic insularity, we analyzed mitochondrial control region sequences within and among badger subspecies ( Taxidea taxus jacksoni Schantz, 1946, Taxidea taxus jeffersonii (Harlan, 1825), and Taxidea taxus taxus (Schreber, 1777)) from nine locations in Canada and bordering United States. Although subspecies designations were supported (a priori subspecific designations, n = 3, AMOVA: FST = 0.40, p < 0.001), insular populations also were found within subspecific ranges as shown by spatial analysis of molecular variation, which suggested that our sample set consisted of five genetic groups (FST = 0.39, p < 0.001). These five distinct groupings included the subdivision of T. t. jeffersonii on either side of the Selkirk Mountains, and of T. t. jacksoni in the western part of its range grouping more closely with T. t. taxus of Manitoba. These results indicate that endangered populations of badgers may be more segregated than previously identified using morphological characteristics as proxies for subspecific designation. These results have important implications for the conservation of badgers in Canada, particularly of the two endangered subspecies.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".