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Documentation and dissemination of the sculptural elements of Canada's Parliamentary Buildings: Methodology development and evolution, a case study

2015· article· en· W1592424465 on OpenAlexaffabout
C. Ouimet, J. Gregg, S. Kretz, Clive Chandler, J.E. Hayes

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 · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsCarleton UniversityPublic Works and Government Services Canada
Fundersnot available
KeywordsDocumentationParliamentSculpturePrecinctGovernment (linguistics)DigitizationArchitectural engineeringCultural heritageArchaeologyCivil engineeringEngineeringHistoryPolitical scienceComputer scienceLawPoliticsTelecommunications

Abstract

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Abstract. Parliament Hill consists of four historic gothic revival buildings, which form part of the Parliament Buildings National Historic Site of Canada in the National Capital of Ottawa. There are more than 2000 masonry sculptural elements throughout the four buildings. Three of the buildings are in the middle of multi-year rehabilitation projects. Extensive Heritage Documentation is being undertaken to support various activities and conservation teams throughout the interior and exterior of the buildings while also serving as a key posterity records. One of the significant heritage documentation projects is the 3D digitization of the 2000+ heritage character defining sculptural elements. The Heritage Conservation Directorate (HCD) of Public Works and Government Services Canada (PWGSC) was tasked by the Parliamentary Precinct Branch (PPB) of PWGSC to document these character defining elements. The sculptures vary in size from as small as 100mm in width to up to 2 meters in size. This project is in its third year and much has been learned and researched about the most appropriate and efficient means by which to document these elements. Although a methodology was in place to document the sculptures at the inception of the project, it has gone through several iterations in order to improve the gathered data, and in turn increase the efficiency, quality and speed of data acquisition. This paper will describe the evolution of the methodology, as well as the rationale for the alterations in technique. With over 600 of the approximate 2000 (heritage character defining) sculptural elements captured to date, the project is entering a critical phase where an efficient and effective method for sharing and disseminating the information to a wide audience is being explored and evaluated. The end result is intended to allow the client (PPB) and the general public a way to look at and interactively manipulate the viewpoint of each digital model. This will provide a unique opportunity for a wide audience to evaluate and appreciate these elaborate works of art. This database of information can also be of use for academic study and research. To date the data captured by HCD has been utilized by conservation professionals and by the Carleton Immersive Media Studio (CIMS) of Carleton University. HCD has partnered with CIMS to explore a wide variety of potential uses for the data including the creation of 2D-drawings, stone carving profile templates, 3D printed scale models, CNC carved maquettes, robotic stone carving, and digitally-designed replacement elements. CIMS and HCD have tested the limits of digital technologies to assist stone carvers in restoring carved elements while also learning from the sculptors and masons to better understand their approach when conserving or restoring a sculptural element. Examples of how the conservation team, specifically the stone carvers have been working with the data and technology will also be presented.

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)0.059
metaresearch head score (Gemma)0.052
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: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.052
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.010
Science and technology studies0.0070.004
Scholarly communication0.0090.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.276
Teacher spread0.244 · 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
Published2015
Admission routes2
Has abstractyes

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