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Record W2775860235

“Lessons learned” in WWI: The German Army, Vimy Ridge and the Elastic Defence in Depth in 1917

2017· article· en· W2775860235 on OpenAlexvenueaboutno aff
Lt.Col. Dr. Christian Stachelbeck

Bibliographic record

VenueJournal of military and strategic studies · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsOffensiveGermanBattleFront (military)LawDoctrinePolitical scienceRidgeCounterattackEngineeringOperations researchHistoryAncient historyGeographyArchaeologyCartographyMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.950

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.130
GPT teacher head0.359
Teacher spread0.229 · 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 designObservational
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

Citations0
Published2017
Admission routes2
Has abstractyes

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