Bibliographic record
Abstract
Within the context of intensified natural hazard events due to climate change, the consideration of human lives and well-being is necessary. One such case of the effect nature has on humanity is through flooding events. For the Ottawa region, this has become an increasingly prominent issue. This study approaches the numbers behind flood risks in Ottawa, particularly surrounding the Rideau River watershed. This watershed was chosen due to the lack of research surrounding the scale of a flood event in this region. The Rideau Valley often consists of rural townships, which are less often outfitted with necessary response measures to natural hazards. This study seeks to use floodplain data as provided by the Rideau Valley Conservation Authority (RVCA) and Ottawa Municipal Open Data sources to map the flood risk areas along the Rideau River and assess the scale of the affected population. This analysis uses ESRI’s ArcGIS program to both create and analyse the overlap between floodplain and housing data. The study also addresses changes in flood risk severity using flood depth data provided by the RVCA, comparing the extent of 100yr and 350yr flooding. Additional open-source data from the Ottawa Neighborhood Study (ONS) allows for analysis of the adjacency to first responders, along with classification of addresses to their closest stations. Dissemination Area (DA) analysis suggests that at least fifty-nine DA districts in Ottawa are within the floodplain of the Rideau River. These districts contain census data including total population (as of 2016), number of dwellings, and population density. The combination of statistical and geographical analysis of these regions will give effective insight to the number of dwellings and estimated population affected in a flood event.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".