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Record W4381283284 · doi:10.25071/2561-5467.1028

Stephen McLaughlin, Russian and Soviet Battleships by M. Stephen Salmon

2023· article· en· W4381283284 on OpenAlexvenueno aff
M. Stephen Salmon

Bibliographic record

VenueThe Northern Mariner / Le marin du nord · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryEconomic history

Abstract

fetched live from OpenAlex

The Northern Mariner / Le marin du nord As for Unbroken, it was sent to the Soviet Navy in 1944, as a part of lendlease to the Soviets and returned in 1949.Sadly, a ship which had defied depth charges and all kinds of seas could not remain unbroken -it was scrapped in 1950.Mars's final words regarding his submarine are poignant ones -he was saddened that his boat was reduced to scrap, but at the same time, he was glad that Unbroken had not rusted away in some dockyard.He hoped, that, through the use of scrap metal, Unbroken would continue to serve the RN in some vastly different form.Perhaps Unbroken still sails today, more than 70 years later, in an even more vastly-changed form.This book is worth reading, as it brings to the reader the immediacy of submarine warfare.The reader feels the crew's fear undergoing depth charge attack.Mars communicates life on board a submarine and ashore in a highdanger zone.The book fills in another gap in the history of the Second World War, but could have been improved by photographs of Unbroken and Mars.A brief biography of Mars could have been included, as the back cover merely states that Mars left the RN due to differences of opinions over postings and service.As a straight reprint, however, such inclusions might have increased the price of the book.Those suggestions aside, this book is recommended.

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)0.001
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.005

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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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