The Perceptions of the Real Estate Sector on Pluvial Flooding in London, Ontario
Bibliographic record
Abstract
The frequency and intensity of pluvial flooding events are increasing in London, Ontario resulting in basement flooding of residential homes. While London has made a number of improvements to its sewer infrastructure to address this issue, their ongoing focus has been to assist homeowners through a grant program as they recognized that increasing awareness of pluvial flood risk and mitigative measures will increase the resiliency of the community. Real estate sector professionals are ideally positioned to discuss the risks and mitigative measures with homeowners before the purchase of a home. Through online surveys and structured telephone interviews, this thesis investigated the perceptions of real estate agents, appraisers, and home inspectors on pluvial flooding in London, their view of homeowners’ awareness, and of the process of disclosing this information during the home purchasing process. Through the Multiple Listing Service and the Seller Property Information Statement, information about the basement flooding risk should be disclosed so that a buyer receives all known risks and mitigative measures of a house.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".