Human Performance Modelling and Mission Planning Optimisation incorporating Human Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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