Bibliographic record
Abstract
The city of Toronto has experienced many major floods over the past century: the flood following hurricane Hazel in October 15, 1954, the August 27, 1976 floods, the August 19, 2005, and the flooding of July 8, 2013. During the latest flooding, some parts of the City of Toronto received over 120 mm of rain, while the monthly average for Toronto is 74.4 mm. The impact was felt as 300,000 residents were affected by power outages. Other serious disruptions included flight cancellations, subway and other transportation closures. It was the most expensive disaster for the province of Ontario. According to the Insurance Bureau of Canada, the damage of the insured properties exceeded $850 million. This event renewed a debate on a number of issues, such as decaying infrastructure, insufficient flood management, and inadequate standards. Don River, the main river crossing the city, is wide but not deep enough, which together with sedimentation contributes to frequent flooding of surrounding areas. In addition, natural creeks have been buried in sewer pipes, thus losing the natural waterways towards the lake Ontario and forcing existing rivers and creeks to overflow their banks. While floodplain maps are generally available, the estimation of flood risk maps based on population, economic development, and critical infrastructure will enhance city's flood mitigation and preparedness planning. In this paper, we present an approach for determining spatial flood risk index map based on population vulnerabilities and terrain morphological characteristics using a geographic information system.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".