CENTENARY OF THE BATTLE OF VIMY (FRANCE, 1917): PRESERVING THE MEMORY OF THE GREAT WAR THROUGH 3D RECORDING OF THE MAISON BLANCHE SOUTERRAINE
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".