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6.4.2 Capability Engineering Process within Canadian Defence: Some Engineering Issues

2005· article· en· W1967518338 on OpenAlexaffabout
Mario Couture, Michel Lizotte, G Dussault, Marielle Mokhtari, François Bernier, Sylvia Lam, Claire Lalancette

Bibliographic record

VenueINCOSE International Symposium · 2005
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsProcess (computing)Work (physics)Engineering managementProcess managementEngineeringSystems engineeringWork in processComputer scienceOperations managementMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Some managerial and technical problems related to the transition of the Canadian military acquisition from the Threat‐Based Planning to the Capability‐Based Planning are identified in this paper. A simplified example of military acquisition illustrates recommendations that were made in the Collaborative Capability Definition Engineering and Management (CapDEM) project to guide the work surrounding the conception of a new Capability Engineering Process. Some elements of solution for the support of such a process are also presented.

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)0.008
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0110.007
Scholarly communication0.0110.005
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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