Bibliographic record
Abstract
The Safety Management System (SMS) provides an environment where undesired events (proactively or reactively identified) are evaluated for the effect on safety using Risk Analysis. When the risk is evaluated, an interim risk reduction (mitigating action) may be applied to reduce the risk to a level that allows operations for a longer period before the safety issue is fully resolved. The risk assessment provides a means of evaluating the risk level and it may be difficult to quantify the “benefit” of interim mitigations that will reduce the risk. Prioritization of issues in the same risk category of the Risk Matrix is often simplified to a schedule and logistics basis of the final corrective action and often does not adequately show the benefit of the interim mitigating actions taken. Employing a concept used in System Reliability Analysis, the Design / Process Failure Modes and Effects Analysis (Design FMEA as defined in SAE J1739) uses a Risk Priority Number as a tool to identify the most significant failure modes that need to be evaluated in order to determine if redesign can mitigate the risk to a lower level. This paper will develop this concept in conjunction with an Aircraft / System Functional Hazard Assessment and acceptable level of risk (certification levels) to formulate a Risk Priority Number as a tool to rank the risks while taking into account the effectiveness of mitigating actions. A means to baseline the product acceptable level of risk / safety is also proposed in order to determine the relative risk increase from the as certified risk level.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".