Ground penetrating radar investigations at the Lake Condah Mission Cemetery: Locating unmarked graves in areas with extensive subsurface disturbance
Bibliographic record
Abstract
Ground penetrating radar (GPR) was used to non-invasively map the location of unmarkedgraves within the Lake Condah Mission Cemetery in western Victoria as a means of siting future interments. This cemetery was associated with the former Lake Condah Mission (established in 1869) and continues to be an important site for local Indigenous people. It is anecdotally thought to contain more than 100 graves however only 26 are currently marked. The GPR survey identified an additional 14 probable unmarked graves as well as 49 other areas that may contain one or more unmarked burials. The extensive subsurface disturbance present at the site and the presence of many extant tree roots made the effective interpretation of the GPR data difficult. Despite this, it was still possible to delineate areas where no unmarked graves are present. This is an important outcome for managing the cultural heritage of the cemetery because it identifies areas where new graves can be emplaced in a culturally appropriate fashion. This demonstrates the utility of GPR as a means of effectively managing heritage sites containing unmarked graves, even when substantial subsurface disturbance is present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".