The Northern and Southeastern Ojibwa: Blood Group Systems and the Causes of Genetic Divergence
Bibliographic record
Abstract
The ABO, MNSs, Diego, Duffy, Kell, Kidd, Lutheran and P blood groups of 105 Ojibwa Indians from Wikwemikong and 96 Ojibwa Indians from Pikangikum are described. Both samples were obtained from reservation populations, the former located on Manitoulin Island in Lake Huron, the latter located in northwestern Ontario. Both populations lack r" and R° which have been identified in the cognate Chippewa Indians of Minnesota.The Pikangikum Ojibwa have the highest incidence of Rz in North America, and the highest incidence of Dia north of Mexico. The Wikwemikong Ojibwa lack Dia, but are distinguished by the presence of B, r, Lua and K, which are not found in Pikangikum. Genic chi-square indicates considerable genetic divergence between the two Ontario Ojibwa populations. Given the small effective population size of the Pikangikum Ojibwa during the past 100 years, genetic drift is probably responsible for the unusual frequencies of Dia, Rz, Ms, and NS on that reservation. Caucasian gene flow is responsible for the occurrence of B, r, Lua and K in Wikwemikong. Estimates of the “aboriginal” frequencies of several blood group, serum protein and red cell enzyme genes in the Wikwemikong Ojibwa are also presented. A highly significant amount of genetic heterogeneity remains between the Ontario Ojibwa after adjustment of the frequencies for gene flow. Genetic drift is considered principally responsible for this divergence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".