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Record W2105466452 · doi:10.1109/naecon.2011.6183090

Using the C-OODA model for CIMIC analysis

2011· article· en· W2105466452 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsIntelligence analysisComputer scienceAction (physics)Military intelligenceDecision support systemDecision analysisCognitive modelArtificial intelligenceOperations researchCognitionEngineeringComputer security

Abstract

fetched live from OpenAlex

In this paper, we use the Cognitive Observe-Orient-Decide-Act (C-OODA) model in a Civil-Military Cooperation (CIMIC) analysis. CIMIC requires intelligent decision making over many activities, variables, and effects. We utilize the Complex Decision Making Experimental Platform (CODEM) from Lafond, DuCharme, and Rioux to provide situation observation, environmental orientation, relational decision making, and action selection, evaluation, and feedback. With the development of complex CIMIC activities, users require effects-based analysis of all civil and military actions for contextual reasoning and situation understanding. For pragmatic information CIMIC system design and analysis, the user (commander or operator/analyst) needs timely and accurate information to conduct proactive actionable intelligence over complex situations. In this paper, we use the C-OODA model in a CIMIC analysis using the CODEM simulation to support the modeling of stability and sustainment operations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.406
GPT teacher head0.331
Teacher spread0.075 · 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