Bibliographic record
Abstract
Currently, available Human Reliability Analysis (HRA) methods were generally developed to support Level 1 Probabilistic Safety Analysis (PSA) models. There has been an increased emphasis placed on Level 2 PSA in recent years; however, the currently used HRA methods are not ideal for this application, including the SPAR-H method. Challenges that will likely be present during a severe accident such as degraded or hazardous operating conditions, shift in control from the main control room to the technical support center, unavailability of instrumentation, and others are not routinely considered for Level 1 HRA analysis. These factors combine to create a much more uncertain condition to be accounted for in the HRA analysis. While the SPAR-H shaping factors were established to support Level 1 HRA, previous studies have shown it may be used for Level 2 HRA analysis as well. The Canadian Nuclear Safety Commission (CNSC) and Idaho National Laboratory (INL) in a joint project are investigating modifications to the SPAR-H method to create more consistency in applying the performance shaping factors used in the method for Level 2 analysis.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".