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Record W4307921855 · doi:10.25071/2561-5467.944

Charles Stephenson, The Eastern Fleet and the Indian Ocean, 1942-1944: The Fleet that Had to Hide by Mark Klobas

2022· article· en· W4307921855 on OpenAlexvenueno aff
Mark Klobas

Bibliographic record

VenueThe Northern Mariner / Le marin du nord · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsAeronauticsIndian oceanHistoryOceanographyGeographyEngineeringGeology

Abstract

fetched live from OpenAlex

Reviews 119 negative concepts of society.Readers can expect a humanistic, but also deeply researched analysis of naval battles.For example, the study of the Battle of Actium (31 BC) recounts the relationships and dramas between Mark Antony, Cleopatra, Cesar Augustus, and Rome that surrounded the battle.This was the story that captured the attention of another author named Shakespeare in another time, prompting him to dramatize the history for theatre.Russell also depicts the struggles of a young Commodore Nelson, a rising naval star, who, at 41 years old, had already sacrificed an eye and an arm fighting for the Royal Navy.He draws brilliantly from the life of Nelson and his mission of "search and destroy," revealing the intrinsic anxiety of the endless "search" while emphasizing his genius, his insecurities, his bravery and his little note to Lady Hamilton.The reader can find these subtle, peculiar and delicate details on every page.The weaker aspects of the book are, firstly, the maps that are located in the initial pages and not among the narratives, which forces the reader to flip back and forth.Secondly, this is not an introductory book: beginners in the naval strategy/history field may find some difficultly with the prolonged details of battles and historical contexts.This, however, makes the book perfect for researchers, especially those in search of more material about ancient naval battles.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.002
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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
Published2022
Admission routes1
Has abstractyes

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