Developing a comprehensive methodology for evaluating economic impacts of floods in Canada, Mexico and the United States
Bibliographic record
Abstract
Assessing the true economic costs of floods is central to addressing their impacts, allocating adequate resources for monitoring and preparedness, assessing their changes over time, and building resilient communities. Considerable variability exists in the choice and implementation of methods used in Canada, Mexico, and the United States at national and sub-national levels for estimating the direct damages and indirect losses caused by floods. This inter- and intra-national variability leads to information gaps when prioritizing development investments, for example, for infrastructure renewal, institutional development, or community enhancements. This paper provides an overview of the range of approaches used in the three countries and analyzes their strengths and weaknesses. It then presents a proposed comprehensive and inclusive methodology that has been developed in close collaboration with a range of stakeholders and domain experts. This methodology builds on existing approaches and offers a comprehensive accounting of costs related to flooding. We offer insights into potential challenges for implementing this methodology across the three countries, particularly related to data availability, access, quality, and spatial coverage. We recommend enhanced gathering data and metadata, and storing it in an information warehouse for their timely dissemination. We also identify the need for further investigation into the definition for “extreme flooding” that incorporates hydrological, societal and economic thresholds, in collaboration between government agencies and the research community.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".