RA-EKI: A use case for collaborative logistics planning in coalition force deployment
Bibliographic record
Abstract
With greater reliance on coalitions for military force deployment, there is a need for organizational collaboration and system interoperability. Interoperability requires full exchange of data, information (contextualized data) and knowledge (actionable information), which ensures that coalition's members will fully collaborate in synchronizing their logistics plans and processes at all echelons. Existing architecture frameworks, such as NATO's Architecture Framework (NAF) and The Open Group architecture Framework (TOGAF), do not support knowledge management, required to process massive amount of data. In order to perform collaborative logistics planning, logisticians and commanders need to access, in a seamless manner, data and information from several domains from systems within the coalition and from external sources such as social media, government and supplier sites, to name a few. The integrated and structured data must then be transformed into information, or contextualized data, and ultimately into actionable information or knowledge. This paper proposes an innovative approach, the Reference Architecture of an Enterprise Knowledge Infrastructure (RA-EKI) that provides a holistic and integrative approach to manage the complete unstructured data to knowledge lifecycle applicable to military logistics planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".