Threat evaluation and weapons allocation in network-centric warfare
Bibliographic record
Abstract
The concepts of threat evaluation and weapons allocation (TEWA) in the defense domain have traditionally been considered from the single platform perspective. However, with the current trend in defense towards network-centric warfare, that is the linking of sensors, engagement systems and decision-makers into an effective and responsive whole, it is becoming more appropriate to view these concepts at the force level. One approach to the challenge of developing force level TEWA functionality is to regard TEWA as a dynamic human decision-making process aimed at the successful exploitation of tactical resources (e.g. sensors and weapons) during the conduct of command and control activities. In this paper, the results of taking this approach to force level TEWA through the application of the applied cognitive work analysis methodology are presented. In particular, a functional abstraction network is described, which encapsulates the inferential transformation from sensor data acquisition to inferences about the identification, intent and level of threat for the given entities in the defense environment. Finally, emerging threat evaluation and weapons allocation concepts in network-centric warfare are outlined and an example is given to illustrate the ideas developed within the paper.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".