Inverse Flood Risk Modelling of The Upper Thames River Basin
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
This report aims to present an alternate approach to climate change impact mod- elling of water resources. The focus of the project is on the analysis of existing wa- ter resources management guidelines specifically targeting critical hydrologic events (extreme floods in this case). The critical hydrologic events are converted to their corresponding meteorologic conditions via use of an event based hydrologic model. The local climatic signal is generated by use of a non-parametric weather generator linked to outputs from a global climate model for three climate scenarios, and their corresponding frequency curves generated. Then, a critical hydrologic event of inter- est is selected, its corresponding meteorological condition obtained, and its frequency of occurrence (one for each climate scenario) determined.\nA scenario selected specifically to study the problem of flooding in the basin showed more frequent occurrence of flooding for nearly all magnitudes of floods. An- other scenario, selected for studying droughts depicts a lesser tendency of extreme flooding events. Therefore, ranges of estimates of changes of frequency of occurrence of critical hydrologic events are obtained in response to changing climatic conditions. Based on these estimates, recommendations for changing current basin management guidelines are provided. They are categorized into three distinct categories: (i) regula- tory (where a review of rules, regulations and operation of current flood management infrastructure are suggested); (ii) budgetary (where investment in new infrastructure, as well as increased maintenance costs of present and future infrastructure, can lead to a need of having higher operating budgets); and (iii) engineering (recommending a review of current design standards of critical infrastructure).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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