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Record W2617419601

Conceiving and Executing Operation Gauntlet: The Canadian-Led Raid on Spitzbergen, 1941

2017· article· en· W2617419601 on OpenAlexaboutno aff
Ryan Dean, P. Whitney Lackenbauer

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsRAIDComputer scienceOperating systemBusiness
DOInot available

Abstract

fetched live from OpenAlex

In August and September 1941, Canadian Brigadier Arthur Potts led a successful but little known combined operation by a small task force of Canadian, British, and Norwegian troops in the Spitzbergen (Svalbard) archipelago in the Arctic Ocean. After extensive planning and political conversations between Allied civil and military authorities, the operation was re-scaled so that a small, mixed task force would destroy mining and communications infrastructure on this remote cluster of islands, repatriate Russian miners and their families to Russia, and evacuate Norwegian residents to Britain. While a modest non-combat mission, Operation Gauntlet represented Canada’s first expeditionary operation in the Arctic, yielding general lessons about the value of specialized training and representation from appropriate functional trades, unity of command, operational secrecy, and deception, ultimately providing a boost to Canadian morale. Interactions also demonstrated the complexities of coalition warfare as well as the challenges associated with civil-military interaction in the theatre of operations.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.689

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0420.014
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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
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

Citations2
Published2017
Admission routes1
Has abstractyes

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