Flood inundation uncertainty: The case of a 0.5% annual probability flood event
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
Aging coastal defences around the UK are challenging managers to redesign schemes to be resilient to extreme events and climate change, be cost-effective, and have minimal or beneficial environmental impact. To enable effective design, reduced uncertainty in the assessment of flood risk due to natural variability within the coastal forcing is required to focus on conditions that pose highest threat. The typical UK standard of protection for coastal defences is to withstand a 0.5% annual probability event, historically also known as a 1 in 200 year return period event. However, joint wave-water level probability curves provide a range of conditions that meet this criterion. We examine the Dungeness and Romney Marsh coastal zone, a region of high value in terms of habitat and energy assets, to quantify the uncertainty in flood depth and extent generated by a 0.5% probability event, and to explore which combinations of wave and water levels generate the greatest threat.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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