SPACE: a new modeling tool for supporting layout design of military command and control spaces
Bibliographic record
Abstract
Military command and control spaces are complex work environments and critical facilities for a military mission. This paper describes a newly developed modeling tool called SPACE (Spatial layout Planning and Analysis for Communication Effectiveness) for assisting layout design of such workplaces. As a Human Factors tool, SPACE provides common functions required for workspace modeling, including rapid workspace prototyping, versatile design visualization, and algorithmic layout assessment. One of its key features is a layout evaluation algorithm that enables objective assessment of floor plans based on their impact on operator communication and interaction efficiency. In this paper, the main functionalities of SPACE are explained using a case study where models were constructed to compare three layout options for a Joint Intelligence Center. The results revealed the pros and cons of each layout in facilitating team interaction involving different sensory domains. While all three layouts were deemed acceptable, an inward-facing boardroom style design was predicted to be optimal as it best balanced the need for direct sightline access, non-technology-mediated verbal conversations, and the physical effort associated with movement to collaborators’ workstations. This study demonstrated the usefulness of modeling and simulation to provide quantitative auditable data for supporting evidence-based decision-making in military system design.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".