Preserving Holocaust history: Geophysical investigations at the Ponary (Paneriai) extermination site
Bibliographic record
Abstract
ABSTRACT Holocaust mass grave sites in eastern Europe can be difficult to investigate due to a paucity of historical documentation relating to the events and because the use of traditional invasive archaeology methods raises concerns around the disturbance of the remains of Jewish people. When combined with other lines of evidence, including historic photos and eyewitness testimony, noninvasive geophysical methods help to effectively identify and demarcate buried features at Holocaust sites, limiting unnecessary excavations. Between 1941 and 1944, as many as 100,000 people were murdered at the Ponary (Paneriai) extermination site in Lithuania, but many critical details of the site layout during this period are still to be resolved, including the location of some of the mass graves and confirmation of an escape tunnel that was used by slave laborers to escape captivity and certain death at the site. At Ponary, a combination of electrical resistivity tomography (ERT) profiling, limited ground-penetrating radar data, and bare-earth elevation data (from a light and distance ranging data set) was used to confirm the location of a large unmarked mass grave with a diameter of approximately 25 m and depth of approximately 4 m. Additional ERT profiling at a second location imaged the entrance to an escape tunnel previously uncovered by an archaeological excavation in 2004, and it detected an approximately 5 m section of the continuation of the tunnel, approximately 33 m away from the tunnel entrance. The geophysical results are supported by evidence from limited archaeological excavations, historical photographs, eyewitness descriptions of the site layout, and testimonies from the few survivors who managed to escape Ponary.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".