Econometric evaluation of large weather events due to climate change: floods in Atlantic Canada
Bibliographic record
Abstract
Climate change increases frequency of large weather events such as floods, storm surges, cyclones, hurricanes, high-speed winds, thunderstorms, snowstorms, blizzards, extreme temperatures, and others. All these events lead to a significant economic damage to property, infrastructure, and human health. Historically Atlantic Canada has been vulnerable to flooding. Therefore, the goal of this study is to establish a relationship between socio-economic, climatological as well as direct flood factors and economic loss from floods in Atlantic Canada. First, this study evaluates probability of floods in Atlantic Canada due to hydrological as well as climatological factors. Second, it tests the hypothesis of an increasing frequency of floods in the future due to climate change. Coupled with economic losses from floods defined earlier, it will give us a possibility to evaluate the expected damage from floods in Atlantic Canada due to climate change to justify investment into mitigation measures.
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How this classification was reachedexpand
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".