Human Performance Modelling and Mission Planning Optimisation incorporating Human Performance
Bibliographic record
Abstract
Factors related to workers and tasks have a combined effect on system performance. Management tends to make effective decisions by understanding the dynamics of the workers' performance over time, affected by their time-varying learning, fatigue, and stress levels. Such managerial decisions could improve system productivity and ensure workforce safety. Despite the importance of the problem, the literature did not focus on the performance modelling or task planning framework that includes all the mentioned factors, i.e., learning, fatigue, and stress. This dissertation includes three main contributions with different performance modelling and task allocation/scheduling planning that help managers plan mission operations, characterized as uncertain, dynamic, and time-sensitive. The first contribution (Chapter 3) is a mathematical model that modifies a popular learning curve model from the literature by making its learning exponent dependent on the fatigue level. The results of applying this model to a data set (vs. the other available ones in the literature,) showed an outperformance in terms of efficiency and balance criteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".