Reproducibility investigation of elicitation techniques in risk assessment for hydraulic turbines
Bibliographic record
Abstract
Certain elicitation techniques exert some control on expert opinions by leading them to a consensus or to a specific choice. In the absence of such guidelines, experts rely on their own knowledge to formulate opinions. This can result in large dispersions and affects the decision maker’s judgment. In this situation, we wonder what the relevant elicitation techniques are and how we can help experts to express their knowledge. From literature review, it is hard to decide if elicitation techniques are equivalent or not, which justifies the reproducibility analysis that we carry out in this paper. In this study, multiple experts have been involved in order to predict the defect size in hydraulic turbines, according to four proposed elicitation techniques. The comparison between these techniques was performed based on a suggested algorithm using the area metric concept. Our Findings show that elicitation techniques with ‘support’ tend to limit variations between experts and might be suitable only when prior knowledge on the expected elicited variable is available. Otherwise, we can end up with a distorted opinion of the elicited variable and an erroneous risk assessment.
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 | 0.218 | 0.554 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".