Sensing houses: New investigations of ground-penetrating radar at Tsimshian Village sites on the northern northwest coast
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Archaeologists have embraced GPR as a powerful tool for exploring subsurface spatial patterns in the archaeological record without excavation. Yet, remote sensing technologies have not been widely applied on the Northwest Coast of North America, largely because the most common anthropogenic site matrix is the heterogenous shell-bearing site (shell midden). The Prince Rupert Harbour (PRH) region (Figure 1), home of the Coast Tsimshian is an example of this geophysical challenge. It has been systematically mapped for over five decades, creating a large inventory of massive shell terraced villages at which geophysical surveys have not been widely employed. The Tsimshian have inhabited PRH for millennia, building monumental winter villages that are represented in the archaeological record and detailed Indigenous oral histories. The Tsimshian had a highly specialized yet diverse marine economy, a keystone resource was shellfish, which resulted in village sites engineered with shell matrices through recurrent deposition from food consumption, but also as a result of massive short-term terracing projects. In this paper, we describe our initial efforts to resolve architectural patterns in this complex archaeological and environmental context and compare the radar results to magnetic gradiometry and low impact ground-truthing results, including sediment coring and mapping of erosion faces. We also discuss the challenges, potential benefits, limitations and efficacy of developing a GPR-based feature confidence index to predict the identity of subsurface archaeological features from geophysical signals in such complex subsurface components. Finally, we consider the utility of GPR as a tool for community heritage management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it