Study on determining the weights of risk assessment indexes based on AHP hierarchical analysis for barracks facilities construction units in highland and alpine areas
Bibliographic record
Abstract
Under the environment of plateau alpine region, the new model of substitute construction separating government construction and management functions has gained great development in barracks construction, which significantly improves the risk management level of barracks facilities to some extent.From the significance of barracks facilities construction guarantee in highland alpine area, the article proposes a risk identification framework for the substitute construction unit of Someplace facilities in highland alpine area based on the whole life cycle of engineering projects.Combined with the risk identification framework, the risk evaluation index system of the agency construction unit is constructed, and then the AHP hierarchical analysis method is introduced to solve the weight of the indexes, and combined with the fuzzy comprehensive evaluation method, the AHP-FCM evaluation model is constructed.A barracks facilities project in a camp area is selected as a case study, and Company T is used as the research object to carry out data analysis of its risk degree using the AHP-FCM model.In the construction of barracks facilities in highland and alpine areas, the biggest risk faced by the construction unit is the project implementation stage, the weight of which reaches 29.93%, and the fuzzy comprehensive evaluation of Company T's risk score is 3.182, which is between medium and large risks.Therefore, the agency needs to examine and check its own risk factors in time, in order to lay a solid foundation for ensuring the smooth implementation of the agency project of barracks facilities in highland alpine areas.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".