PHOTOGRAMMETRY FOR THE EPIGRAPHIC SURVEY IN THE GREAT HYPOSTYLE HALL OF KARNAK TEMPLE: A NEW APPROACH
Bibliographic record
Abstract
The purpose of this paper is to present a method to carry out a computerized epigraphic survey of inscriptions engraved on columns. In fact, the epigraphic survey of Egyptian temples is essential to understand and to reconstitute these ancient monuments, and hieroglyphic engravings of columns are as important as the scenes that appear on walls. Columns often bear cartouche friezes and ritual episodes that give information on the date of the temple or on the nature of the activities that took place in its hypostyle hall. Furthermore, they sometimes describe techniques used by the architects to build the hall. It is therefore necessary to find a way to keep the texts engraved on the numerous temples ’ columns, as on the 134 columns of the Karnak Hypostyle Hall. Nowadays, epigraphic surveys are still for the most part done in a traditional handmade fashion, while computer-aided epigraphic surveying is only used for simple tasks, such as drawing the contour of hieroglyphic signs on scanned photographs. Different methods of survey are carried out, but practically only on plane surfaces. The GRCAO of the University of Montreal and the laboratory MAP-PAGE of the INSA Strasbourg have conducted research together. Using photogrammetry, they made it possible to survey and to register the hieroglyphics inscriptions engraved on conical or cylindrical surfaces. The present paper explains the adjustment and programming of general photogrammetric formulae for the three-dimensional reconstitution of a column and the two-dimensional surveying of its epigraphy, based on a series of snapshots of the column’s surface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".