Little Ship, Big Screen: Animating the Battle of the Atlantic at the Canadian War Museum
Bibliographic record
Abstract
The Canadian War Museum developed a large-screen immersive computer-animated video experience for visitors to the Second World War gallery. It is a dramatisation of a Canadian corvette’s nighttime encounter with a German U-boat in the North Atlantic, told from the perspective of the corvette’s crew while escorting a convoy. This article examines and discusses the process of developing the upgrade, including decisions about subject matter and approach, and design and accessibility. The role of archival and historical research in defining these objectives is detailed, together with a behind-the-scenes look at the development and installation of the final product. Le Musée canadien de la guerre a conçu une expérience vidéo immersive animée par ordinateur et présentée sur un grand écran pour les personnes qui visitent la galerie de la Seconde Guerre mondiale. Il s’agit d’une dramatisation d’une rencontre nocturne entre une corvette canadienne et un sous-marin allemand dans l’Atlantique Nord, racontée du point de vue de l’équipage de la corvette, qui escorte un convoi. Le présent article examine le processus de réalisation de la mise à jour de la galerie, y compris les décisions relatives au sujet et à l’approche, à la conception et à l’accessibilité. Le rôle de la recherche archivistique et historique dans la définition de ces objectifs est décrit en détail. L’article nous plonge également dans les coulisses de la réalisation et de l’installation du produit final.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".