The Probability and Management of Human Error
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Embedded within modern technological systems, human error is the largest, and indeed dominant contributor to accident cause. The consequences dominate the risk profiles for nuclear power and for many other technologies. We need to quantify the probability of human error for the system as an integral contribution within the overall system failure, as it is generally not separable or predictable for actual events. We also need to provide a means to manage and effectively reduce the failure (error) rate. The fact that humans learn from their mistakes allows a new determination of the dynamic probability and human failure (error) rate in technological systems. The result is consistent with and derived from the available world data for modern technological systems. Comparisons are made to actual data from large technological systems and recent catastrophes. Best estimate values and relationships can be derived for both the human error rate, and for the probability. We describe the potential for new approaches to the management of human error and safety indicators, based on the principles of error state exclusion and of the systematic effect of learning.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it