Application of re/insurance models to estimate increases in flood risk due to climate change
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
Floods are the most common and most expensive natural hazard, and they are expected to become more frequent as the climate changes. This article presents research that used re/insurance catastrophe models to estimate the influence of climate change on flood-related losses. The geographic focus of the study was the Canadian Maritimes—specifically Halifax, Nova Scotia—and it sought to determine how municipal risks due to rainfall-driven riverine floods could change as a result of climate change. Findings show that annual flood losses could increase by up to 300% under a business-as-usual climate scenario by the end of the century (i.e., no mitigation or adaptation), even without accounting for changes to the built environment that could increase exposure (e.g., no population or economic growth). Increasing flood risk demands an open discussion about how much risk is acceptable to the community and what controls on further growth of exposure are necessary. Moreover, projected increases in flood losses put into question long-term insurability in the Halifax area, and highlight a broader problem facing manyother areas in Canada as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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