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DIGITAL TOOLS FOR DOCUMENTING AND CONSERVING BAHRAIN’S BUILT HERITAGE FOR POSTERITY

2017· article· en· W2747582522 on OpenAlexaffabout
Davide Mezzino, Luigi Barazzetti, Mario Santana Quintero, A. El-Habashi

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsCarleton University
Fundersnot available
KeywordsDocumentationPromotion (chess)Process (computing)EngineeringStudioWorld Wide WebArchitectural engineeringComputer sciencePolitical science

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.006

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.

Opus teacher head0.033
GPT teacher head0.270
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2017
Admission routes2
Has abstractyes

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