Clan, language, and migration history has shaped genetic diversity in Haida and Tlingit populations from Southeast Alaska
Bibliographic record
Abstract
The linguistically distinctive Haida and Tlingit tribes of Southeast Alaska are known for their rich material culture, complex social organization, and elaborate ritual practices. However, much less is known about these tribes from a population genetic perspective. For this reason, we analyzed mtDNA and Y-chromosome variation in Haida and Tlingit populations to elucidate several key issues pertaining to the history of this region. These included the genetic relationships of Haida and Tlingit to other indigenous groups in Alaska and Canada; the relationship between linguistic and genetic data for populations assigned to the Na-Dene linguistic family, specifically, the inclusion of Haida with Athapaskan, Eyak, and Tlingit in the language family; the possible influence of matrilineal clan structure on patterns of genetic variation in Haida and Tlingit populations; and the impact of European entry into the region on the genetic diversity of these indigenous communities. Our analysis indicates that, while sharing a "northern" genetic profile, the Haida and the Tlingit are genetically distinctive from each other. In addition, Tlingit groups themselves differ across their geographic range, in part due to interactions of Tlingit tribes with Athapaskan and Eyak groups to the north. The data also reveal a strong influence of maternal clan identity on mtDNA variation in these groups, as well as the significant influence of non-native males on Y-chromosome diversity. These results yield new details about the histories of the Haida and Tlingit tribes in this region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".