DIGITAL WORKFLOW FOR THE CONSERVATION OF BAHRAIN BUILT HERITAGE: THE SHEIK ISA BIN ALI HOUSE
Bibliographic record
Abstract
Abstract. Currently, the commercial market offers several tools for digital documentation of historic sites and buildings. Photogrammetry and laser scanning play a fundamental role in the acquisition of metric information, which is then processed to generate reliable records particularly useful also in the built heritage conservation field. Although potentially very fast and accurate, such techniques require expert operators to produce reliable results, especially in the case of complex and large sites. The aim of this paper is to present the digital workflow developed for data acquisition and processing of the Shaikh Isa Bin Ali house in Muharraq, Bahrain. This historic structure is an outstanding example of Bahrain architecture as well as tangible memory of the country history, with strong connotations in the Bahrain cultural identity. The building has been documented employing several digital techniques, including: aerial (drone) and terrestrial photogrammetry, rectifying photography, total station and laser scanning. The documentation project has been developed for the Bahrain Authority for Culture and Antiquities (BACA) by a multidisciplinary team of experts from Carleton Immersive Media Studio (CIMS, Carleton University, Canada) and Gicarus Lab (Politecnico di Milano, Italy).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".