3D characterization of the Mila 18 archaeological site in Warsaw, Poland: From imaging to excavation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Archaeological site investigations in urban environments are often beset with challenges such as (1) an absence of buried artifacts due to recent disturbance from infrastructure development or (2) community concerns about potential site impacts from excavations. Noninvasive geophysical surveys that use a combination of methods can help mitigate the risks of uncertain outcomes by identifying areas where culturally significant features are more likely to be uncovered. We show how new technology and traditional geophysical survey methods were used to characterize the subsurface of the Mila 18 Memorial site in Warsaw, Poland. This site is one of the most important places of remembrance for the Holocaust and coincides with the location of an underground bunker that was used by Jewish resistance groups during the 1943 Warsaw Ghetto Uprising. In this case study, we showcase the use of drone multispectral imaging and handheld lidar scanning in conjunction with other geophysical techniques including electrical resistivity tomography, ground-penetrating radar, magnetic gradiometer, twin-probe resistance, and fixed-frequency electromagnetic surveying. The geophysical results were included in an interactive 3D site model to help identify a suitable site for excavation. To document the excavation and to validate and further interrogate the geophysical survey results, we used lidar-based photo-textured scans of the excavation that were incorporated into the 3D site model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it