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CENTENARY OF THE BATTLE OF VIMY (FRANCE, 1917): PRESERVING THE MEMORY OF THE GREAT WAR THROUGH 3D RECORDING OF THE MAISON BLANCHE SOUTERRAINE

2017· article· en· W2748073052 on OpenAlexaboutno aff
Arnadi Murtiyoso, Pierre Grussenmeyer, S. Guillemin, Gilles Prilaux

Bibliographic record

VenueISPRS annals of the photogrammetry, remote sensing and spatial information sciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsBattleFortress (chess)GermanHistoryOperations researchComputer scienceEngineeringArchaeologyAncient history

Abstract

fetched live from OpenAlex

Abstract. The Battle of Vimy Ridge was a military engagement between the Canadian Corps and the German Empire during the Great War (1914-1918). In this battle, Canadian troops fought as a single unit and won the day. It marked an important point in Canadian history as a nation. The year 2017 marks the centenary of this battle. In commemoration of this event, the Pas-de-Calais Departmental Council financed a 3D recording mission for one of the underground tunnels (souterraines) used as refuge by the Canadian soldiers several weeks prior to the battle. A combination of Terrestrial Laser Scanner (TLS) and close-range photogrammetry techniques was employed in order to document not only the souterraine, but also the various carvings and graffitis created by the soldiers on its walls. The resulting point clouds were registered to the French national geodetic system, and then meshed and textured in order to create a precise 3D model of the souterraine. In this paper, the workflow taken during the project as well as several results will be discussed. In the end, the resulting 3D model was used to create derivative products such as maps, section profiles, and also virtual visit videos. The latter helps the dissemination of the 3D information and thus aids in the preservation of the memory of the Great War for Canada.

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)0.002
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 score0.866
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.277
Teacher spread0.224 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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