Application of Analytic Hierarchy Process (AHP) in shipyard project investment Risk Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Risk Recognition is an important part in shipyard project risk management. The purpose of this paper is to explain how to identify risks by means of AHP. Firstly, we analyzed briefly the superiority of AHP in shipyard project risk Recognition; secondly, expounded the basic steps of risk Recognition based on AHP in shipyard project investment; then we proposed the principle and tips of applying AHP in identifying project risks by demonstrating a case of shipbuilding base. To prove the validity of AHP, we have identified the risk factors of the Shipyard project that mentioned in the case above, and have also calculated the influence weights taxis of dominating risk factors to the general risk. Key words: Shipyard Project Investment; AHP; Risk Recognition; Risk Factors
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it