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Record W4253426047 · doi:10.1109/wsc.2015.7408355

Battlefield simulations for Canadian Army Indirect Fire Modernization options analysis

2015· article· en· W4253426047 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue2015 Winter Simulation Conference (WSC) · 2015
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceAmmunitionAdversaryBattlefieldModern warfareOperations researchNavyAeronauticsComputer securitySimulationEngineering

Abstract

fetched live from OpenAlex

Computerized battlefield simulations were conducted for an Operational Research and Analysis study by the Land Force Operational Research Team for the Canadian Army Indirect Fire Modernization project. The goal was to assess the relative strengths of a set of Indirect Fire options. The simulations were designed on Python programming language, with the SimPy package, and utilized data collected in workshops with subject matter experts. The simulation had multiple scenarios, probabilistic distributions of tasks and task frequencies and targets depending on the size and capability of the enemy threat. Options considered in the project consisted of 81 mm mortars, 120 mm mortars, M777 light-weight towed howitzers and rockets. Emphasis was placed on data collection to ensure the inclusion of relevant scenarios and identification of weapons systems specifications for the model. Indirect Fire asset usage, ammunition consumption and task success were the main results.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.090
GPT teacher head0.316
Teacher spread0.226 · 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