Enhancing Workload Assessments for Validation Activities Associated with DBA and BDBA Scenarios
Bibliographic record
Abstract
After the Fukushima Daiichi accident, nuclear regulators around the world have required that power reactor licensees develop more extensive emergency mitigating responses and severe accident management provisions beyond the defense-in-depth measures for design-basis accidents previously in place. Workload assessments represent common validation techniques that are used to demonstrate that workers are able to perform tasks without unacceptable performance degradation. High workload is known to induce stress and fatigue and may severely diminish a worker’s capacity to perceive, recognize, and respond appropriately during emergency or unanticipated events, which may result in undesirable consequences. In estimating workload during emergency and severe accident scenarios, power reactor licensees tend to rely on subjective measures of workload, such as the NASA Task Load Index. Because of reported mismatches in the literature between subjective and physiologically derived estimates of workload, it is prudent to see what more can be done to improve the current state of practice in the context of emergency and severe accident conditions.To improve confidence in workload estimates, it is advocated that the nuclear industry integrate physiologically based measures into current practices by making use of on-body or wearable physiological sensors. In this paper, an overview of three different approaches to the empirical measurement of workload is provided. The advantages of wearable physiological sensors are considered in the context of extreme environments and occupations, with tangible examples including heat stress and pupillometry. Suggestions for a consensus forum on workload are provided, and a research plan directed at improving the current practice of workload estimation is offered for consideration.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".