Risk Management Techniques: A Review and Study in Dealing with Coronavirus Disease of 2019 (COVID-19)
Bibliographic record
Abstract
COVID-19 has been a global issue since its first case in November 2019.In March 2020, an increase in the statistics of this pandemic occurred worldwide.The direct exposures, such as human resource loss, and the indirect exposures, such as the systemic loss, have affected the individuals and the companies during the COVID-19 pandemic.The identified loss exposures could be measured using several methods to implement suitable actions for the management of the identified risks.However, it has been indicated from the analysis that a minimum of five months is required for it to be solved.Therefore, the losses due to COVID-19 could be managed using three different risk management techniques, including the risk control techniques, which do not involve money and risk financing techniques.Meanwhile, the alternative risk transfer under the aforementioned techniques involves the investment in capital.Notably, these techniques must be performed by three distinct parties: the individuals, companies, and the government.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".