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Record W7055084506

Area Bombing by Day: Bomber Command and the Daylight Offensive, 1944â1945

2012· article· en· W7055084506 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2012
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsOffensiveStrategic bombingGermanDaylightAmmunitionWorld War IIPretext
DOInot available

Abstract

fetched live from OpenAlex

This article will examine an important but neglected phase of the Allied strategic bomber offensive in the Second World War. Given the very rich literature on the bombing war it is surprising to discover that litle attention has been paid to the daylight attacks undertaken by Royal Air Force (RAF) Bomber Command in the fall and winter of 1944–1945. Nowhere in the existing literature is there a systematic analysis of this period of operations when the RAF and Royal Canadian Air Force (RCAF) carried out 153 daylight raids between 27 August 1944 and 24 April 1945. Two primary issues will be addressed. The first concerns the accuracy achieved by Bomber Command in its daylight missions. The second is to determine if the reintroduction of daylight attacks resulted in Bomber Command carrying out a different and more selective targeting policy. Both of these issues are related to the more general question of the role played by Air Marshal Sir Arthur Harris in shaping the policy of Bomber Command. Harris’s name is usually associated with doctrinaire commitment to area bombing in general and the destruction of German civilian housing in particular. The evidence presented in this essay will allow the reader to form a more complete picture of Harris’s repsonse to the changing circumstances of the war.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.009
GPT teacher head0.179
Teacher spread0.171 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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