“Lessons learned” in WWI: The German Army, Vimy Ridge and the Elastic Defence in Depth in 1917
Bibliographic record
Abstract
In late 1916, the German 3rd Supreme Army Command (OHL) under General Hindenburg and General Ludendorff decided to go onto a strategic defensive on the main front in the west. With the help of the tactical lessons learned in the battle of materiel at the Somme, the OHL developed the principle of elastic defense in depth. It consisted of mounting a mobile and offensive defense that granted the forces limited room to withdraw and was primarily based on immediate counterstrokes being conducted by reserves. For this purpose, the bulk of the force would no longer be concentrated in the front line, but were to disperse in a several kilometer deep position area. This new doctrine, which was by no means uncontested among German commanders, faced the crucial test when the Anglo-Canadian attack on Vimy Ridge /Arras was launched on 9 April 1917. As a consequence of blatant leadership errors and a flawed implementation of the principles of elastic defense, the German defenders lost Vimy Ridge at the beginning of the battle. Nevertheless, the allied breakthrough attempt ended in early May 1917 in failure and with severe losses. This paper shows how the German side learned from the battles at Vimy/Arras and optimized its system of elastic defense for the forthcoming defensive operations in 1917.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".