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Digitally-Assisted Stone Carving of a Relief Sculpture for the Parliament Buildings National Historic Site of Canada

2015· article· en· W2177639722 on OpenAlexaffabout
J.E. Hayes, Stephen Fai, S. Kretz, C. Ouimet, Patrick White

Bibliographic record

VenueISPRS annals of the photogrammetry, remote sensing and spatial information sciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsPublic Works and Government Services CanadaCarleton University
Fundersnot available
KeywordsCarvingDominionParliamentRedevelopmentSculptureWorkflowEngineeringArchitectural engineeringArchaeologyVisual artsCivil engineeringComputer scienceHistoryPolitical scienceLawArtMechanical engineeringDatabase

Abstract

fetched live from OpenAlex

Abstract. The emerging field of digital fabrication is a process where three-dimensional datasets can be directly transferred to fabrication equipment to create models or even 1:1 building elements. In this paper, we will discuss the results of a collaboration between the Carleton Immersive Media Studio (CIMS), the Dominion Sculptor of Canada, and the Heritage Conservation Directorate (HCD) of Public Works and Government Services Canada (PWGSC), that utilizes digital fabrication technologies in the development of a digitally-assisted stone carving process. The collaboration couples the distinguished skill of the Dominion Sculptor with the latest digital acquisition and digital fabrication technologies for the reconstruction of a deteriorated stone bas-relief on the façade of the East Block building of the Parliament Buildings National Historic Site of Canada. The intention of the research is to establish a workflow of hybrid digital/analogue methodologies from acquisition through rehabilitation and ultimately to the fabrication of stone elements.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.269
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations18
Published2015
Admission routes2
Has abstractyes

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