Identification of a Pre-Contact Polar Bear Victim at Native Point, Southampton Island, Nunavut, Using 3D Technology and a Virtual Zooarchaeology Collection
Bibliographic record
Abstract
ABSTRACT. The skeletal remains of an adult Sadlermiut woman with obvious trauma to her cranial and post-cranial skeleton were excavated from Native Point (KkHh-1), Southampton Island, Nunavut, in 1954. In order to determine the possible cause of this damage, we first documented the skeletal injuries using traditional bioarchaeological techniques. We then created a three-dimensional model of the cranium and mandible to permit better visualization and analysis of the cranial lesions, some of which were obscured by post-depositional weathering. This model was imported into a virtual environment in order to compare the lesions with the craniodental structure of four Arctic carnivore species available as digital models through the Virtual Zooarchaeology of the Arctic Project (VZAP), an online comparative faunal collection. We eliminated all but the polar bear (Ursus maritimus) using this process, which suggested that an individual of this species was responsible for the skeletal trauma. We further identified a minimum number of “bites ” on the cranium, some with overlapping lesions, which suggested a possible attack sequence. Use of a virtual environment and an online comparative collection were critical to this process and represent a new technique for evaluating past skeletal trauma and its causes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".