Providing the Developmental Model of a Comprehensive System of Prioritizing Occupational Risks Using Analytical Hierarchy Process (AHP) Technique in the Management of Integrated Health, Safety and Environment (Case Study of Oil Industrial Contractors)
Bibliographic record
Abstract
In this study, researchers designed a functional decision supporting model for workplaces for managers and showed its application by implementing it in health, safety and environment fields. Researchers used Excel software to demonstrate the ease of use of this method. This model is designed to help managers for decision-making to select and allocate resources to optimize risk of irreversible and unacceptable risks. In this regard, detailed quantitative risk assessment methods and techniques AHP expert’s extraction, classification and has been structured; to help managing workplaces risks and reducing the effects other than representing the applications of multi-criteria decision making techniques especially Analytical Hierarchy Process (AHP) technique, and in the end, to attribute resources in the best possible way. This model comes as a decision support system and is used to ease, the accuracy of decisions and reassuring them. This study suggests the use of multi-criteria decision-making techniques in large-scale projects and organizational risk management process for managers and reassuring them in terms of efficiency and effectiveness of steps taken to reduce and control occupational risks in the workplace and promise reduction irreparable effects for both organizations and for society, and to optimize the allocation of limited resources of organization. To carry out all stages of computational and analytic hierarchy process technique, Excel software was used in this study to show that this technique with capacity is also applicable in various companies.
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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 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".