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Record W163796348 · doi:10.5555/1999416.1999420

Modeling and analysis of Canadian forces RSOM hubs for northern operations

2010· article· en· W163796348 on OpenAlexaffabout
Ahmed Ghanmi

Bibliographic record

VenueSummer Computer Simulation Conference · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFacility Location and Emergency Management
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsSoftware deploymentLift (data mining)Computer scienceOperations researchOperations managementEngineeringData mining

Abstract

fetched live from OpenAlex

This paper presents an analysis of a Reception, Staging and Onward Movement (RSOM) hub concept to support Canadian Forces (CF) Northern operations. RSOM hubs are permanent or temporary staging bases for cross-loading between strategic and tactical lift during military deployment and sustainment operations. Performance measures were developed to assess the effectiveness and the responsiveness of different hub options. An optimization model was also developed to determine the optimal number and locations of hubs. Deployment scenarios to different Northern locations were simulated and assessed. Sensitivity analysis was conducted to examine the impact of different operational parameters on hub performance. The study indicated that the RSOM hub concept would offer potential cost avoidance and response time reduction on deployment lift for Northern operations and could be a potential strategy for improvement of the CF domestic support capability.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.063
GPT teacher head0.270
Teacher spread0.207 · 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 designSimulation or modeling
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
Published2010
Admission routes2
Has abstractyes

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