‘Buried Beneath the Waves’: Using GIS to Examine the Physical and Social Impact of a Historical Flood
Bibliographic record
Abstract
Natural disasters such as floods can periodically disturb and destroy the built and social fabric of communities. Despite their importance, specific ramifications of natural disasters can be overlooked in local histories due to a paucity of data. In this article we bring together several disparate sources of data within a historical geographic information system (HGIS) to study certain physical and social details of the flood which devastated the Town of London West, Canada on 11 July 1883. The integration of historical and contemporary data sources allow for the construction of a three-dimensional model of where the flood likely occurred. With the location of the flood determined, it is possible to discern which residents were impacted and the legacy of the disaster on the community. This study demonstrates how digital technologies such as GIS can help provide a richer understanding of urban and environmental history.
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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) | 0.000 | 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".