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Record W7021029653

Mobility in the Arctic: applying lessons from the past to the new operational environment.

2022· other· en· W7021029653 on OpenAlexaboutno aff

Bibliographic record

VenueIke Skelton Combined Arms Research Library (CARL) Digital Library (US Army Combined Arms Center) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionTSG101Gestational periodHyporeflexiaDiafiltrationArticular cartilage damagePretextDemotion
DOInot available

Abstract

fetched live from OpenAlex

The Arctic operational environment has and continues to present challenges to mobility at the tactical and operational level. Doctrine and training solutions require perspectives from the past as well as an understanding of the future developments to the operational environment. Three case studies were analyzed to inform doctrinal and training recommendations – the Battle of Suomussalmi (7 December 1939-8 January 1940), the Petsamo-Kirkenes Operation (7-29 October 1944), and post-World War II Canadian exercises (1945-1955). The outcomes of this analysis when held against future Arctic factors of temperature, permafrost, precipitation, infrastructure, vegetation, and the Russian threat indicate that US doctrine provides a sufficient base to inform tactical mobility. Operational doctrine, however, needs to be updated to fully prepare commanders and staff for the mobility challenges awaiting an Arctic Brigade. Continuing multi-national training exercises across the Arctic, developing a permanent joint multinational training center, and standardizing Arctic training will further prepare ground forces for the mobility challenges of the future.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.276
Teacher spread0.244 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

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