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Record W3130813266

The Unpredictable Course of Naval Innovation – The Guns of HMS Thunderer

2021· article· en· W3130813266 on OpenAlexaffvenue
Ben Lombardi

Bibliographic record

VenueJournal of military and strategic studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsNavyAeronauticsMuzzleLawCourse (navigation)EngineeringHistoryPolitical scienceOperations managementManagementOperations researchArchaeologyAerospace engineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

In January 1879, a muzzle-loading gun aboard HMS Thunderer, one of the Royal Navy’s most powerful warships, exploded. A parliamentary investigation determined that the accident occurred because of human error brought about by a highly innovative, but complicated, loading mechanism. Given earlier unsatisfactory experience with early breech-loading guns, contemporary naval engagements and expectations of the future nature of conflict at sea, retention of muzzle-loaders seemed a reasonable course of action. Vast sums were, therefore, spent in ensuring that Britain’s navy had the biggest and most powerful of that type of ordnance. But the explosion and other advances in gun design meant that muzzle-loaders were a dead end, and the incident on Thunderer became the impetus for the Royal Navy to adopt breech-loaders. This incident shines light upon the thinking within the Royal Navy at the time regarding advanced guns. But it also underscores the uncertainty and unpredictability that is inevitably attached to rapid innovation by a large military institution such as the Royal Navy was in the late-19th century. This story is highly relevant to force development considerations today because in any era of continuous technological change, mistakes are inevitable and their expectation should be accommodated within planning.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.335
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2021
Admission routes2
Has abstractyes

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