A PHOTOGRAMMETRIC WORKFLOW FOR RAPID SITE DOCUMENTATION AT STOBI, REPUBLIC OF NORTH MACEDONIA
Bibliographic record
Abstract
Abstract. The so-called “Theodosian Palace” is one of the most significant Late Antique structures at the site of Stobi, in the Republic of North Macedonia. Popularly thought to be a stopping-place of Theodosius I on his way through the province of Macedonia Secunda according to the evidence of the Codex Theodosianus, the structure is in dire need of conservation with many of the stone and mortar walls threatening to collapse onto the mosaic floors below. Any conservation effort in the Republic of North Macedonia must produce rigorous documentation before any physical work can take place. The most important and time consuming component of the project preparation are section and elevation drawings documenting each of the walls stone-by-stone, with elevations and scales indicated in a format prescribed by the state. These drawings are usually done manually on graph paper in the field, with the assistance of time-honoured manual tools – the plum-bob and tape-measure –, but this method is enormously time consuming and has considerable of room for error. The present project, begun in 2016 and the subject of this paper, endeavoured to show that new, photogrammetric methods could not only improve the accuracy of these drawings, but also the speed with which they are made. Our results demonstrate an increase in accuracy by an order magnitude, from 3 cm to 3 mm, and an improvement in the time to deliver the final product from an estimated 8 months to 2 months.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".