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Record W7055499405

The Defence and Evacuation of the Kuban Bridgehead,
\nJanuary – October 1943

2014· dissertation· en· W7055499405 on OpenAlexaboutno aff

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2014
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsGermanOffensivePeninsulaPort (circuit theory)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines German and Soviet operations in the Kuban area of southern Russia
\nduring January – October 1943. As the bulk of German Army Group A withdrew from the
\nCaucasus in early 1943 to avoid encirclement following the Soviet counter-offensive at
\nStalingrad, Seventeenth Army was ordered to hold a bridgehead on the Kuban Peninsula as
\na jumping-off point for a future resumption of the German offensive into the Caucasus.
\nIn early February, the Soviets attempted to eliminate the German bridgehead through a
\ncombined amphibious and ground operation. The ground operation did not achieve any
\nsignificant gain, and the main amphibious landing operation was a catastrophic failure, but a
\nsecondary landing succeeded in gaining a foothold in the southern suburbs of the port city of
\nNovorossiysk that was quickly expanded and became known as Malaya Zemlya (The Small
\nLand).
\nEarly April saw the launch of Operation Neptune, a German effort to eliminate the Malaya
\nZemlya beachhead. This failed utterly due to the weakness of the German assault groups
\nand the tenacious Soviet defence. The Soviets then launched a series of attempts through
\nthe spring and summer to break the German line, with minimal success. The final phase of
\noperations in the Kuban was the withdrawal of Seventeenth Army by sea and air across the
\nKerch Strait to the Crimea. Almost a quarter of a million men and the bulk of their equipment
\nwere successfully evacuated, with very light losses.
\nThe thesis examines some factors that contributed to the success or failure of these
\noperations and also considers why a region that was of key strategic interest in both German
\nand Soviet planning in the first period of the war quickly diminished in importance and has
\nbeen largely neglected in the published history 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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.

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
Published2014
Admission routes1
Has abstractyes

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