Risk-informed decision-making in asset management as a complex adaptive system of systems
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
Decision-making is an essential activity in asset management (AM). It is influenced by various factors (strategic, technical/technological, economic, organisational, regulatory, safety, markets, etc.). Sound decision-making in AM ought to take into account relevant factors in order to balance risks, opportunities, performance, costs and benefits. Additionally, modern organisations evolve in complex operational and business environments and are exposed to significant uncertainties. In such a context, decision-making in AM becomes more challenging. This study proposes a holistic three-step risk-informed decision-making (RIDM) methodology developed for AM, where RIDM is considered a complex adaptive system of systems. The methodology is applied in a case study to analyse possible modification strategies for a nuclear power plant's emergency core cooling system. Through the RIDM process, quantitative models and other factors have been taken into account in order to obtain the necessary comprehensive insights regarding the decision to be made.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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