Bibliographic record
Abstract
The safe operation of technical systems is a mandatory basic requirement for the entire industry. However, there are specific industries where the safety of operation is critical and is considered as a required characteristic. These types of industries include the aerospace, military, civil aviation, nuclear power, as well as chemical and automotive industries. Safety is everyone's responsibility but engineering plays the most important role in the course of achieving a safe product operation. There are two specific phases of the product life cycle where the safety characteristics should be addressed by engineering activities: the design and development and operation phases. Modern engineering education is oriented to provide future engineers with a sufficient background to be able to Conceive-Design-Implement-Operate. The emphasis of this approach is on the achievement of dual objectives; first to teach the students a large spectrum of technologies and second to develop their personal and interpersonal capabilities in order for them to be able to build complex engineering systems. A university-level engineering curriculum very rarely includes topics related to safety and security of technical systems. Engineering programs mainly focus on the product performance knowledge domains and the safety of a product is covered in a somewhat underwhelming way. This paper provides an overview of selected university-level educational programs focusing on the topic of technical systems' safety. The synopsis of the educational safety initiative is described as a combination of university and industry integrated approach to enhance the safety knowledge and to create a safety-oriented culture for the new generation of engineers. A few examples of implemented programs from the aerospace industry are provided.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.294 | 0.126 |
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".