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Record W2994587968

Can Information Displace Mass? Armour In The Future Operating Environment

2019· article· en· W2994587968 on OpenAlexvenueno aff
J.C. Maerz

Bibliographic record

VenueJournal of military and strategic studies · 2019
Typearticle
Languageen
FieldEngineering
TopicMilitary Strategy and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFirepowerStalemateInfantryBattleArmourDominance (genetics)AmmunitionArtilleryHard powerVietnam WarEngineeringPolitical scienceForensic engineeringLawHistoryArchaeologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

Mobile and heavily protected forces are mainstays in the conduct of land combat. The platform best exemplifying the characteristics of mobility, protection and firepower for land forces is the tank. The last century of land warfare featured the dominance of the tank as a decisive tool of battle. Tanks were crucial to overcoming the stalemate of the trenches during battles in the last years of the First World War. They decided battles in the European, North African, and Eastern theatres during the Second World War. Heavy armoured forces formed the nucleus of a credible conventional deterrent force during the Cold War. Most recently, tanks delivered unparalleled protection to ground forces and lethal fires as an infantry support weapon during counter insurgency operations in Iraq and Afghanistan. Maintaining a heavy armour capability, however, comes at significant costs in terms of capital, personnel and resources. In an era of increased resource competition and where technological advances promise to offset conventional applications of hard power, many question if tanks remain operationally relevant.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0190.034
Open science0.0010.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0360.007

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 head0.205
Teacher spread0.196 · 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 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
Published2019
Admission routes1
Has abstractyes

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