Bibliographic record
Abstract
The key to a successful engineering project isthe early identification of hazards, followed by designingthe process in such a way as to remove the hazard, or todevelop means to mitigate the risk. A Hazard is aninherent property of a substance or device which cancause harm to people, property, the environment, or to abusiness (loss of reputation). Any one hazard can cause aloss through a number of hazardous events. Each eventhas a specific loss quantity and frequency. Risk is theproduct of the consequence and the frequency/probabilityof the hazardous event.A learning module, that introduces the concepts of hazardand risk, has been developed, which is targeted at upperyear engineering students. The module has examplesfrom various disciplines and leads through the conceptsof hazard identification and hazard reduction. Riskquantification is introduced, as are several techniques forrisk quantification. Finally, the management of risk,including the management of residual riskThe module was provided to a class of 3rd year chemicalengineering students, to supplement an existing course inwhich inherently safer design techniques are taughtthrough use of case studies and a design project. Studentfeedback will be presented. The module will be availablefor use in fall of 2014.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.399 | 0.165 |
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".