Bibliographic record
Abstract
i The goal of this learning module and instructor guide is to introduce students to the complex subject of Human Reliability Analysis (HRA) within the curriculum of an undergraduate engineering course. It is also assumed that students in the course are familiar with the topic of human factors. The module meets criteria determined in a collaborative effort between Professor Jamieson of the University of Toronto and an industry request from Atomic Energy of Canada Limited (AECL). Upon completion of the course module and subsequent review of material students will be able to:- describe the development, purpose, principles and applications of HRA;- discuss strengths, weaknesses and alternatives;- Research additional resources in the area of HRA; and- Understand examples of HRA in practice. To obtain the objectives the document is divided into three Parts: Part I- Human Reliability Analysis, Part II- Instructor’s Guide, and Part III- Examples of Applied ASEP. Part I is essentially intended to familiarize the instructor with the material. Part II is intended to summarize the material especially relevant to meeting the above criteria. Part II also includes insight as to how the material supports learning objectives. Part III is intended to provide the instructor with practical applications to impart to the student. Part
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.698 | 0.608 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".