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Record W4406824083 · doi:10.25071/2561-5467.1249

Przemyslaw Budzbon, Jan Radziemski, and Marek Twardowski, Warships of the Soviet Fleets, 1939-1945. Volume II: Escorts and Smaller Fighting Ships

2025· article· pl· W4406824083 on OpenAlexvenueno aff
Charles Ross Patterson

Bibliographic record

VenueThe Northern Mariner / Le marin du nord · 2025
Typearticle
Languagepl
FieldSocial Sciences
TopicHistorical Geopolitical and Social Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsVolume (thermodynamics)Ancient historyAeronauticsPolitical scienceHistoryEngineeringPhysics

Abstract

fetched live from OpenAlex

This work is the second entry in a multipart tabulation of all known Soviet warships from the era of the Second World War, including civilian conversions and Lend-Lease vessels.This particular volume addresses escorts and smaller warships, with Volume I having covered major combatant vessels and the succeeding Volume III focusing on auxiliary craft.Less a traditional book and more an extremely detailed database, this compendium by Budzbon, Radziemski and Twardowski offers researchers and readers a comprehensive English-language source on the myriad array of often-forgotten vessels that were fielded by the Soviet Union during the war years.Surviving period photographs placed throughout the work, alongside new outboard profile and top-down renderings, help expand the information's effectiveness, with a quick-reference index at the end to aid in location of individual ships.As stated earlier, this is not a traditional scholarly work, so it begins without preamble or analysis.Instead, two key maps are printed on the work's endpapers illustrating the main Soviet naval bases and shipyards and an extensive alphabetical acronym guide provides both the Romanized Russian words and English translations.From here, the authors delve into the eleven warship categories discussed in the work, continuing from Volume I by having the first section, "Escort Ships," labeled as section 11.Each section is further divided into subsections such as Soviet-built vessels, civilian conversions, Lend-Lease ships, and war prizes, with each class entry largely following the same layout.The layout begins with a statement of the name of the class or converted vessel, along with any relevant Soviet Project Numbers.Basic data

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: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.003
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.008

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 head0.011
GPT teacher head0.224
Teacher spread0.212 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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