Bibliographic record
Abstract
This paper addresses the advantages as well as the obstacles in practicing photogrammetry based on archival photos of archaeological sites and examines how the results can be put to use for further research, preservation, restoration and monitoring rates of deterioration. While the extensive use of historic aerial photographs has been applied to photogrammetric modeling, archaeological excavation archives have been largely ignored. Historically archaeological excavations have been vigorously documented photographically and many of these photographs are available in archives. Not all photo archives are suitable for photogrammetry as they were not photographed with the intention of overlap and other photogrammetric qualities. By selectively choosing photographs with common points and manipulating exposures, cropping and other properties to enhance commonality, 3D models of past structures and excavations can allow us to revisit them, produce accurate measurements and view angles that were never photographed. Some sites are still available for modern comparison and surveying, allowing us to quantitatively compare conditions at the time of excavation with the current state of those sites. Given the right treatment, retrospective photogrammetry will have impacts in the preservation, restoration and monitoring of the deterioration of archaeological sites. Examples from the Athenian Agora: the state prison and Omega House, and Ancient Corinth: the Fountain of the Lamps, will be used to demonstrate these possibilities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".