Evaluation of the barriers to and drivers of the incorporation of unmanned aerial vehicles into dam asset management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dam asset management entails the consistent evaluation of the state and usefulness of dams as tangible assets in terms of their expected lifespan, criticality, operational history, maintenance history, and long-term financing plan. This research aims to identify and evaluate the critical drivers and barriers associated with the use of unmanned aerial vehicles in dam asset management. This study uses rough decision-making trial and evaluation laboratory and interpretive structure modelling to analyze the interactions that take place between the barriers to and the drivers of unmanned aerial vehicles incorporation. To overcome the problem of vagueness, it develops rough set theory to determine the driving and dependence power of the drivers and the barriers, respectively. According to the findings, the most significant barrier to the incorporation of unmanned aerial vehicles into dam asset management is a lack of skilled operators, while the most significant driver of their incorporation is their cost-effectiveness. The findings of this study will be highly valuable to practitioners and government agencies working on the implementation of unmanned aerial vehicles in dam asset management, as they will enable them to correctly assess the different aspects of unmanned aerial vehicles incorporation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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