Determination of Human Error Probabilities for the Maintenance Operations of Marine Engines
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
Human error is a crucial factor in the shipping industry and not to mention numerous human errors occur during the maintenance procedures of marine engines. Determination of human error probabilities (HEPs) is important to reduce the human errors and prevent the accidents. Nevertheless, determination of HEPs in the maintenance procedures of marine engines has not been given desired attention. The aim of this study is to determine the HEPs for the maintenance procedures of the marine engines to minimize the human errors and preclude accidents from the shipping industry. The Success Likelihood Index Method is used to determine the HEPs due to the unavailability of human error data for maintenance procedures of marine engines. The results showed that among the 43 considered activities in this study, inspection and overhauls piston/piston rings have the lowest HEP meaning it has a lower consequence for accidents. On the other hand, fuel and lubricating oil filters pressure difference checking and renews filter elements activity have the highest HEP indicating it has high chances for accidents.
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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 | 0.004 | 0.005 |
| 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