The Importance of Minimally Invasive Remote Sensing Methods in Huron-Wendat Archaeology
Bibliographic record
Abstract
Abstract The Huron-Wendat have had their ancestors’ villages and burial sites investigated archaeologically for over 170 years. Past and ongoing land disturbance and invasive archaeological excavation have erased dozens of Huron-Wendat village sites in Ontario, hindering Huron-Wendat duty to care for their ancestors. Consequently, over the last 20 years, in addition to large-scale repatriation of ancestral remains, the Huron-Wendat have requested that archaeologists make every effort to avoid any further excavation of ancestral sites. This poses a new challenge for archaeologists about how to learn about the Huron-Wendat past with minimal disturbance to ancestral sites. Honoring the cultural responsibilities of the Huron-Wendat, the authors have employed minimally invasive remote sensing methods of investigation on Ahatsistari, a forested early seventeenth-century Huron-Wendat village site in Simcoe County, Ontario. Remote sensing methods (e.g., magnetic susceptibility survey, high-resolution soil chemistry sampling, and metal detector survey) have revealed village limits and the possible location and orientation of longhouses, providing essential information in support of the Huron-Wendat imperative to find, assess, and preserve as many of their archaeological sites as possible. This is to protect the ancestors, learn from the ancestors, and preserve ancestral sites and related landscapes for future generations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".