Application of Ground-Penetrating Radar to Mapping Archaeological Features at the Gould Site, Port au Choix
Bibliographic record
Abstract
GROUND-PENETRATING RADAR (GPR) is a geophysical technique used to help identify and characterize archaeological sites prior to excavation. GPR is particularly well suited to archaeological surveys because it provides a nondestructive, rapid way to provide continuous high-resolution profiles across a site. Because all information is acquired at the ground surface, there is no disturbance of buried cultural material. Data are collected at a walking pace, permitting a large area to be surveyed relatively quickly, particularly when compared to the amount of time required for hand excavation (Figure 1a). Although post-collection processing of the data is possible, and in most cases is desirable, many systems provide the almost immediate creation of a paper or electronic version of the survey record, allowing the investigator to review data in the field. This feature allows the survey pattern to be altered or refined immediately to allow closer investigation of anomalous areas. Application of standard values for electromagnetic wave propagation rates calculated for a variety of materials permits two-way travel times to be converted to depth rapidly, and aids in archaeological assessment of a site while in the field. In this paper we explore the application of GPR to the pre-excavation analysis on an area of the Gould Site (EeBi-42), Port au Choix (Figure 2a). Earlier excavations at the site provided “ground truth” for on-site survey decisions. Excavations after the GPR survey allowed testing of data interpretation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".