Sensing houses: New investigations of ground-penetrating radar at Tsimshian Village sites on the northern northwest coast
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".