Bibliographic record
Abstract
A straightforward, general, and simple methodology is given for evaluating and predicting the human error probability (HEP) in transients for accident risk prediction and reduction purposes. The result is validated against all the available data, producing a completely independent assessment of the uncertainty in HEP for safety and risk analysis, and a new validated prediction method. Previous published work established the technical basis for the existence of learning curves for predicting human performance, system outcomes, and accident rates, which was derived and validated against extensive system outcomes and human learning trial data. This latest second-generation minimum error rate equation (MERE)/universal learning curve (ULC) prediction is based on the well-known and proven learning hypothesis. It is therefore applicable to transient and accident analysis, providing the probability of failure or success for individuals as well as for systems. In this paper, new simulator data and three major existing human reliability analysis (HRA) methods used in safety and risk analysis (e.g. technique of human error rate prediction (THERP), human error and assessment technique (HEART), and human cognitive reliability (HCR)) are benchmarked against the new prediction derived directly from the general ULC. Since these existing methods utilize empirical factors, expert judgement and task analysis, the paper demonstrates a totally new objective approach to benchmark human reliability analysis.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".