Adaptation tipping points and opportunities for urban flood risk management
Bibliographic record
Abstract
The effects of climate change are expected to increase the frequency and magnitude of floods, droughts and heat waves. An emerging method termed adaptation tipping point – opportunity (ATP-O) assesses a system's climate-incurred tipping points and uses opportunities arising from urban developments to introduce adaptation strategies while reducing investment costs. The objective of this research was to apply the ATP-O method to the city of Dordrecht in the Netherlands. The results show that the alternative adaptation strategy proposed (an overland drainage system) would be effective in coping with the effects of climate change where the current management strategy (disconnection of impervious surfaces from sewer systems) fails to do so. The ATP-O also proved helpful in identifying opportunities to adapt at lower costs. This research stimulated discussions between stakeholders on performance objectives, policy development, investment strategies, and flood risk management practices. The sensitivity analysis performed to support such discussion revealed that small variations in acceptability thresholds, associated with policy objectives, can have significant impact on ATP occurrence and timing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".