The Untapped Potential of Magnetic Survey in the Identification of Precontact Archaeological Sites in Wooded Areas
Bibliographic record
Abstract
Abstract Evaluating the archaeological potential of wooded areas is often difficult because many of the techniques archaeologists commonly use to locate and map archaeological sites elsewhere are less effective in the trees. Ground cover hinders the visual identification of surface artifacts during pedestrian survey, and the tree canopy impedes many of the techniques used to map areas of interest, such as optical theodolites and DGPS. Shovel test pitting, which disturbs the integrity of sites and provides limited contextual information, is the most common method used to evaluate woodlots today. In light of increasing interest from Indigenous peoples in limiting the impact of archaeological work on their cultural heritage, we are testing less invasive methods to locate and map archaeological sites within wooded areas. Here, we present the results of a magnetic susceptibility survey on a wooded precontact site in southern Quebec, where the technique rapidly determined site limits and pinpointed the location of several longhouses and other features. Where geological conditions are suitable, this method could considerably reduce the cost and impact of archaeological assessment and investigation of wooded sites by both cultural resource management (CRM) and academic archaeologists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".