Survey Déjà Vu: Lessons Learned from the Archaeological Re-mapping of a Métis Overwintering Settlement
Bibliographic record
Abstract
Although the advantages of archaeological remote sensing have long been known, the techniques have still not been fully incorporated into standard archaeological practice. Drawing upon the example of an archaeological remote sensing survey conducted in April 2022 and subsequent excavation in July 2022 at the Chimney Coulee site (DjOe-6) in Saskatchewan, we demonstrate the value of the integration of remote sensing methods early and throughout an entire project. Over the span of five days, we were able to use drone-based light detection and ranging (LiDAR) and orthoimagery, ground-penetrating radar (GPR), and magnetic gradiometry alongside more traditional archaeological survey methods to survey the site and locate two probable late nineteenth-century Métis cabins. The use of remote sensing techniques allowed for the efficient identification of future excavation areas and comparisons to previous mapping work and generated new questions about the site. This paper provides a methodological example of non-invasive archaeological survey for non-specialists and demonstrates how students and early career researchers can play an important role in the advancement of Canadian archaeology by experimenting with new ways of conducting archaeological survey and mapping.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".