Human Factors and Ergonomics in Transportation Control Systems
Why this work is in the frame
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Bibliographic record
Abstract
Employing case studies taken from work experience in the UK, USA, Canada and Japan this paper observes the evolution of Human Factors (HF) and ergonomics in the railroad from a practitioner's point of view. Practical areas for application of HF at specific points in railroad signaling and control systems are described. HF considerations in advanced train control systems and the movement towards automation are discussed as well as the impact of these new technologies on the context of operation itself. There is now a greater reliance on the operator to remain vigilant and react efficiently when intervention on automation is required both within the control room and driver cab environments. This paper illustrates some of the human performance concerns for novel transportation control systems that are faced today and discusses how this area of cognitive attention, human error and workload is difficult to assess and predict.
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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