Lessons learned during Covid-19 from engineering asset management of dams
Bibliographic record
Abstract
Public and private owners of critical infrastructures all over the world are taking high-quality standards to face the consequences of pandemics, particularly critical infrastructure such as dams that needs more attention to maintain and operate during coronavirus disease (Covid-19) pandemics. In this study, critical strategies have been identified through literature review and with the support of experts’ opinions. The rough Decision-making Trial and Evaluation Laboratory and interpretive structural modelling methods were integrated to determine the most important strategies that were identified by literature review and experts’ opinions. Moreover, the methodology was used to find the relationships, cause and effect between the critical strategies. Interviews were completed with professional managers and experts in the field of dam operation and maintenance to help in finding the influence degree between these critical strategies. Among 11 initial strategies, six critical strategies were selected for this study from the experts’ points of view. By applying Matriced Impacts Croisés Multiplication Appliquée á un Classement analysis, driving and dependence powers were also determined and classified for these strategies. The outcomes indicate that the strategy of reviewing emergency action plans and planning for how routine and unplanned work will be implemented during pandemic staffing restrictions is the most driving among these strategies in dam asset management in Canada during pandemics.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".