A Fuzzy Expert System for Task Distribution in Teams under Unbalanced Workload Conditions
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Bibliographic record
Abstract
Inappropriate workload levels on the team members of a naval force have been detected as a problem that can threaten the performance and safety of future naval operations. A suitable distribution of tasks among the members of a team is a crucial issue in order to prevent high and low workload levels. In this paper, we propose a rule-based expert system, the task distribution expert system (TDES), which assists team leaders to manage mental workload in a team by suggesting appropriate task assignments. The TDES emulates the behavior of a team leader deciding which member of the team should perform a task and how. The system handles mental workload as an uncertain fuzzy concept comprising three fuzzy variables that represent the way mental workload affects performance. Automation issues and different recommendations for effective workload management in teams are analyzed and incorporated. A prototype demonstrates the system
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it