Close-Range Photogrammetry for Documenting and Enhancing Thamudic Rock Art and Epigraphy
Bibliographic record
Abstract
The eastern frontier of Roman Empire was ethnically and culturally diverse. Yet despite the abundance of evidence for such diversity, we know relatively little about the peoples who populated the Roman frontier. A good case in point is the Thamudic people of the Hisma desert in modern day southern Jordan. The written historical record provides only two references from the classical period, along with a handful from the later Islamic period, all of which are contradictory. What is more, the sources give virtually no hints about their way of life. The archeological record is not much more revealing. In fact, the only tangible traces we have of these people are the vast numbers of inscriptions and petroglyphs they left behind. These inscriptions, however, can often be difficult to study due to centuries of weathering and vandalism, both ancient and modern. A way to document and enhance this material in a quick and accurate way in harsh, remote environments is urgently needed. Close-range photogrammetry with digital SLR cameras provides the ideal solution. Three-dimensional data from photogrammetry is quick to capture in the field, extremely accurate and requires nothing other than a consumer camera and software for post-processing. Using software adapted from the mining industry we can use a process known as depth-mapping to enhance even extremely shallow incisions and to reveal texts that have been damaged, or even intentionally erased. By doing so, we can gain a better understanding of the lives of these people on the Roman frontier.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".