An inventory and risk-based prioritization of Steep Creek Fans in Alberta, Canada
Bibliographic record
Abstract
In June 2013, heavy rainfall caused flooding on most rivers in the province of Alberta, Canada, producing one of Canada’s most expensive natural disasters with about $6 billion (CDN) in damage. Flooding inundated several municipalities including downtown Calgary, the fourth-largest city in Canada. Debris flows and debris floods caused extensive highway closures and damages to development on alluvial fans. Following these events, the Government of Alberta requested an inventory of all fans intersecting municipal development, major roads and highways in Alberta. Such fans may be subject to debris flow, debris flood (mud flows), and/or flood hazards. The study area spans the entirety of the Alberta Rocky Mountains, approximately 51,000 km2 (7% of Alberta). We characterize 710 fans in terms of hazard level and presence and types of elements at risk. We statistically analyse watershed attributes to predict the dominant fan hydrogeomorphic process types. All fans under provincial jurisdiction are assigned priority ratings based on hazard levels and the presence and value of elements at risk. The prioritization is risk-based as it considers both hazards and potential consequences. Of the fans prioritized, 13% intersected parcels containing land and residential developments with an assessed value of $2.4 billion (CDN), and the remainder were crossed by roads, pipelines or transmission lines. We present the study results on an interactive, searchable web application that can support ongoing hazard and risk assessments and risk reduction planning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| 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 | 0.001 |
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".