Graphic war navy the secret naval drawings and illustrations of World War II
Bibliographic record
Abstract
"Prepare to embark on an extraordinary journey into the heart of naval warfare during World War II with Graphic War Navy. Following the success of Graphic War: The Secret Aviation Drawings and Illustrations of World War II, Donald Nijboer unveils a treasure trove of top-secret drawings that have remained unpublished. This meticulously curated collection showcases a wealth of training manuals, vibrant wartime posters, and captivating illustrations. Scouring archives around the world, Nijboer has included naval material from Great Britain, The United States, Germany, and Canada. One of the highlights of Graphic War Navy is the stunning cutaway drawings of naval vessels and their armaments. These visuals played a pivotal role in the strategies of both Allied and Axis forces. In an era where there was limited intelligence about enemy craft, these illustrations were indispensable. Delve into the world of anonymous graphic artists and technical illustrators who, though unrecognized in their time, left an indelible mark on history. Their work, revived within these pages, offers a rare glimpse into war room tactics and the training of personnel. Whether you're a naval enthusiast, a modeller, an artist, or simply someone fascinated with World War II history, Graphic War Navy promises an enthralling insider's view of the epic battles fought in the vast expanse of the open sea."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.149 | 0.034 |
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".