DIGITAL TOOLS FOR DOCUMENTING AND CONSERVING BAHRAIN’S BUILT HERITAGE FOR POSTERITY
Bibliographic record
Abstract
Abstract. Documenting the physical characteristics of historic structures is the first step for any preventive maintenance, monitoring, conservation, planning and promotion action. Metric documentation supports informative decision-making process for property owners, site managers, public officials, and conservators. This information serves also a broader purpose, over time, it becomes the primary means by which scholars, heritage professionals, and the general public understand a site that radically changed or disappeared. Further, documentation supports monitoring as well as the character-defining elements analysis, relevant to define the values of the building for the local and international community. The awareness of these concepts oriented the digital documentation and training activities, developed between 2016 and 2017, for the Bahrain Authority for Culture and Antiquities (BACA) in Bahrain. The developed activities had two main aims: a) support the local staff in using specific recording techniques to efficiently document and consequently preserve built heritage sites with appropriate accuracy and in a relatively short period; b) develop a pilot project in collaboration with BACA to validate the capacity of the team to accurately document and produce measured records for the conservation and management of Bahrain built heritage. The documentation project has been developed by a multidisciplinary team of experts from BACA, Carleton Immersive Media Studio (CIMS), Carleton University, Canada and a contracted researcher from the Gicarus Lab, Politecnico di Milano (POLIMI) in Italy. In the training activities, the participants have been exposed to a wide range of recording techniques, illustrating them the selection criteria for the most suitable one, according to requirements, site specifications, categories of values identified for the various built elements, and budget. The pilot project has been tested on three historical structures, both with strong connotations in the Bahrain cultural identity: the Shaikh Isa bin Ali house, Aljazzaf house and the Siyadi Majlis. These two buildings, outstanding examples of Bahrain architecture as well as tangible memory of the country history, have been documented employing several digital techniques, including: aerial and terrestrial photogrammetry, rectifying photography, total station and 3D laser scanning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".