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Record W4400139408 · doi:10.25071/2561-5467.1182

A.J. Chapman, The War of the Motor Gun Boats. One Man’s Personal War at Sea with the Coastal Forces. 1943-1945 by Robert L. Shoop

2024· article· en· W4400139408 on OpenAlexvenueno aff
Robert L. Shoop

Bibliographic record

VenueThe Northern Mariner / Le marin du nord · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary and Defense Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAeronauticsHistoryPsychologyEngineering

Abstract

fetched live from OpenAlex

The war at sea in the Second World War encompassed many different aspects of that struggle.Much has been written about the war waged by German U-Boats in the Atlantic and as well, the battles involving the surface fleets.Much less has been written regarding a critical part of the war at sea: the confrontations between German Schnellboots (S-boats -torpedo boats), generally known as E-Boats for "Enemy Boats") and the British Royal Navy's Coastal Forces.In The War of the Motor Gun Boats, A.J. ("Tony") Chapman has written a memoir of his part in the war at sea.When the Second World War broke out in September 1939, the British Royal Navy had very few assets to patrol the British coasts.By contrast, the German Kriegsmarine ("war navy") had a force of well-designed E-Boats available to confront British shipping.Quickly, British shipbuilders developed several boats that would carry the brunt of the coastal war.British coastal forces had three basic classes of combat boats: Motor Torpedo Boats ("MTBs") which were meant to attack enemy shipping by launching torpedoes.MTBs at first were not heavily armed with machine guns or heavier weaponry, so Motor Gun Boats ("MGBs") were developed to escort MTBs and attack German E-Boats.These craft tended to be small in size, made of wood, and fast.A third type of coastal force boat, the Motor Launch, ("ML") was larger than MTBs or MGBs but heavily armed.(It should be noted that as the war progressed, MTBs were progressively up-gunned and MGBs carried torpedoes, so the distinction between MGBs and MTBs became essentially non-existent.)Throughout the Second World War, the E-Boats and MGBs, MTBs, and MLs were fierce opponents.Author Chapman joined the Royal Navy in 1942 after living through the early bombing of Great Britain.As a teenager, he volunteered as an ARP messenger and spent many nights in air raid shelters during the 1940-1941 Luftwaffe Blitz.During one particularly arduous night bombing raid, he earned a commendation for carrying messages while bombs were falling near him.When Chapman reached the age of 17½, he realized he faced the British draft.Not wanting to be called into the British Army or Royal Air Force, he went to a Royal Navy (RN) recruiting station, enlisted, and was given a choice of RN branches.He was classified as a radio operator and was told he would be called up when he turned eighteen.Six months later, Chapman was officially part of the RN.His description of RN basic training is valuable as it preserves a part of the military too

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)0.002
metaresearch head score (Gemma)0.003
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 score0.100
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0150.005

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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
Published2024
Admission routes1
Has abstractno

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