An integrated remote sensing approach to Métis archaeology in the Canadian Prairies
Bibliographic record
Abstract
Abstract Indigenous communities are increasingly turning to archaeological remote sensing to help detect and preserve the material remains of their pasts without extensive excavations. For some Indigenous communities, histories of dispossession and mobility have led to a disconnection between them and archaeological sites, leading to challenges with collaborative models emphasizing local engagement. Here, we present a case study where the nearest modern community associated with the site is hundreds of kilometres away but preserving their heritage is important. The Métis, an Indigenous nation, emerged alongside the North American fur trade during the seventeenth century and became a distinct people. During the nineteenth century, the Métis adopted a highly mobile lifestyle, centred around bison hunting, where they would form temporary villages to overwinter on the northern plains. This case study highlights a post‐contact application of remote sensing where the targets are not large colonial structures but more ephemeral and short‐term occupation sites. We applied multiple methods, specifically ground‐penetrating radar (GPR), magnetic gradiometry, multi‐spectral and orthographic UAV imagery, to survey the mid‐nineteenth‐century Métis wintering site of Chimney Coulee (DjOe‐6), near Eastend, Saskatchewan, Canada. Using high‐frequency GPR (900 MHz) and magnetic gradiometry, this survey successfully delineated the wood wall remains and chimney of one cabin, later confirmed through targeted excavation. The survey was then expanded to other higher potential areas of the site to help refine the approach and define other areas of interest. Here, we outline the results of the Chimney Coulee project and our progress in developing an integrated remote sensing approach for Métis sites. We conclude that there are many forms of collaboration to which remote sensing can contribute and that careful survey can provide insights for modern groups' sense of identity, homelands and Indigenous rights.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".