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Record W223752060

Restraint in Urban Warfare: The Canadian Attack on Groningen, Netherlands, 13-16 April 1945

2013· article· en· W223752060 on OpenAlexaboutno aff
Jeffrey D Noll

Bibliographic record

VenueIke Skelton Combined Arms Research Library (CARL) Digital Library (US Army Combined Arms Center) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFirepowerLimitingDilemmaPopulationPolitical sciencePoliticsLawComputer securityEngineeringGeographySociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Urban terrain presents significant tactical challenges to attacking armies, limiting weapons effects and mobility while disrupting formations and command and control. The human terrain in cities creates a tactical dilemma, placing large civilian populations in close proximity to the fighting. The issue of restraint in urban warfare has been described as a modern phenomenon, with urban warfare in World War II characterized as unlimited. In April 1945, however, the Canadian Army limited its firepower while attacking the city of Groningen, Netherlands to limit damage and civilian casualties. This thesis examines the reasons for these restraints and the methods used to balance those restraints with accomplishment of the mission. The Canadians limited their use of force for political reasons based on intent from the British. They accomplished their mission due to intelligence gained from the friendly population, local fire superiority gained by tanks and flamethrowers, and the ineffectiveness of the poorly organized and equipped German defense. This thesis provides a historical case study of the reasons for restraint in urban warfare and the tactical challenges associated with such limitations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.686

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0220.007
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.043
GPT teacher head0.289
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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