A Rapid Response Survey to Characterize the Impacts of the 2017 High Water Event on Lake Ontario
Bibliographic record
Abstract
Abstract In the spring and summer of 2017, communities along the Lake Ontario shoreline suffered from the worst flood event on record. In late May, daily water levels reached their highest point in over 100 years, and flooding continued throughout much of the summer as lake levels slowly declined, with inundation and erosion significantly impacting shoreline homes and businesses. In this work, we present results from a rapid response online survey of property owners along the New York Lake Ontario shoreline to quantify the perceived flood impacts of the 2017 extended high water event. The survey focused on the degree and spatial distribution of inundation and erosion; the duration and drivers of inundation; the associated damages to different property features, with an emphasis on shoreline protection; and the degree of disruption to business and other activities and services. Photographic documentation of inundation extent and property damage also was provided by survey respondents. We demonstrate the potential utility of this dataset by characterizing key features of inundation and erosion impacts across the shoreline, and by using classification and regression trees to explore the predictability of inundation and erosion based on property characteristics. This work is part of a larger effort to develop models of inundation and erosion that can support flood impact assessments across the shoreline and help communities better prepare for future extended high water events.
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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 | 0.001 | 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) | 0.004 | 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 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".