A New Method for Spatial Analysis of Risk in Water Resources Engineering Management
Why this work is in the frame
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Bibliographic record
Abstract
Uncertainty in water resources management is in part about variability, in part about ambiguity. Both are associated with lack of clarity because of the behavior of all system components, lack of data, lack of detail, lack of structure to consider the water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. The two major sources of variability are temporal and spatial heterogeneity. Temporal variability occurs when values fluctuate with time. Other values which are affected by spatial variability are dependent upon location of an area. A major part of the water resources management risk confusion relates to an inadequate distinction between the objective risk (real, physical) and subjective (perceived) risk. Because of the confusion between the two concepts, many characteristics of subjective risk are believed to be valid also for objective risk. The main objective of this paper is to present the possible methodology for the reliability analysis of water resources systems that will be capable of: (a) addressing water resources uncertainty caused by variability and ambiguity; (b) integrating objective and subjective risk; and (c) assisting the water resources management based on better understanding of spatial variability of risk. Presented methodology is illustrated using flood reliability analysis of the Medway Creek floodplain in the City of London, Ontario, Canada.
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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