Bibliographic record
Abstract
Abstract Risk refers to the possibility and the fear of things going wrong (i.e., some combination of events that have negative impact) and the magnitude of the losses resulting from these events. The concept of risk varies depending on the perception of different individuals and in some cases the “perceived” or “risk‐neutral” probabilities of events are more important than the real‐world probabilities, because they drive publicly traded asset prices in the immediate term. The Basel committee provides a framework for regulating minimum capital requirements for banks to cover losses incurred under five different types of risk: credit risk, market risk, operational risk, liquidity risk, and legal risk, and many of these categories carry over to different types of industry. Complex structures or organizations are exposed to many different risk factors or types of risk. The probability of one or more risk events is often very small and difficult to assess for lack of historical experience. There is a relationship among risk factors that may increase the probability of them occurring in combination. Because of the complexity, simulation methodology is quickly becoming the method of choice for evaluating and providing safeguards against the potential losses resulting from risk exposure. In this article, we discuss the use of Monte Carlo simulation as a cost‐effective method to quantify the financial risks of a corporation.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".