Human-Machine Teaming through Stochastic Structural Equation Modeling in SMR Control Rooms
Bibliographic record
Abstract
Small Modular Reactor (SMR) control rooms present novel human-machine teaming challenges due to high automation, multi-unit monitoring, and reduced staffing levels. Understanding how operators' cognitive states and trust in automation relate to interface design preferences is crucial for safe and efficient SMR operations. This paper applies a stochastic structural equation modeling (SEM) approach to survey data from 30 nuclear control-room operators to quantify latent factors of Situational Awareness (SA), Workload (WL), and Trust in Automation (TR). A confirmatory measurement model validates these latent constructs from multi-item questionnaire scales. The structural model then examines how trust in the automated system influences operators' situational awareness, and how SA in turn affects perceived workload. To capture individual differences and evolving preferences, we extend the model with a latent class (mixture) component, revealing potential operator subgroups with distinct cognitive-trust profiles. Results indicate that higher trust in automation is associated with significantly better situational awareness (0.4, p<0.01), which correlates with lower selfreported workload (-0.5, p<0.01). The stochastic extension identified two latent operator classes: one "high-trust" group showing strong TR→SA effects and corresponding lower workload, and another "low-trust" group with attenuated TR→SA linkage and generally elevated workload. These findings underscore the importance of designing SMR interfaces and training programs that foster appropriate trust and support operators' situational awareness, thereby managing cognitive workload. The study demonstrates the value of stochastic SEM in human-machine systems research, offering a rigorous quantitative tool to inform adaptive interface design and personalized operator support in next-generation nuclear power plants.
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 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".