Project risks’ management model on an industrial entreprise
Bibliographic record
Abstract
© Canadian Center of Science and Education. The article proposes complex model of project risks’ management on an industrial enterprise, including interrelation of work stages in risk management, project risks’ evaluation and management methods and instruments; and an integrated index as an element of risks analysis technique. Project risk analysis and evaluation process takes one of the major places in procedural aspect. Risk management begins with the quality risk analysis where risks are identified and grouped. Results of quality risk analysis are used for the subsequent quantitative risk analysis which includes their evaluation in three key parameters: probability of a risk event, level of expected losses, limits of manageability of risks. Integrated index for risks’ analysis and evaluation developed by the author considers risks’ dual nature, probabilities balance, realization consequences and risks’ manageability. The function of this integrated index is identification of the project risks which can be influenced the most. Based on the calculation of integrated indexes of the identified project risks the decision on primary management for the risks with greater integrated indexes is made. The main procedure after the quantitative risk analysis of the risk management stage is to choose the risk management method and its subsequent application. It is necessary to analyze and generalize risk management activity efficiency, risk factors and uncertainty in the project finale. All the integrated information goes to an organization databank for further use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".