GPR Surveying at a Maya Ruin Site, Belize, Central America
Bibliographic record
Abstract
Since 2000, The University of Calgary has conducted a range of near-surface geophysical surveys (including 3C-3D high-resolution seismic) at the ancient Maya site of Maax Na in Belize, Central America. This exploratory work has been undertaken in cooperation with a team of archaeologists. The purpose of using these non-invasive and non-destructive methods is to detect anomalies that could be associated with caves, burials, caches, or other interesting shallow structure. Distinct anomalies are then assessed for future analysis and excavation. Our GPR surveys have been conducted using Sensors and Software's NOGGIN Smart-Cart system with a 250 MHz antenna. A number of 2-D lines and 3-D grids have been acquired across a large plaza during the last several years. We found that the saturation state of the near surface considerably alters the radar velocity (dry=0.122-0.140m/ns and wet=0.058-0.106m/ns). The depth of penetration, in the jungle-littered plaza, is about 2 – 3 m. A host of point diffractors, which originate due to changes in electrical and magnetic properties in the subsurface or point sources such as sharp edges or rocks, have been identified on a number of the GPR lines and prioritized for further analysis. In 2004, one of the more interesting anomalies was flagged for excavation. Archaeologists excavated a 1×1metre pit and identified all the lots associated with particular stages of plaza development and history. This archaeological “ground truth” is correlated with the GPR events.
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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 | 0.000 |
| 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 | 0.000 |
| 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".