Reach-Based Extrapolation to Assess the Ice-Jam Flood Hazard of an Ungauged River Reach along the Mackenzie River, Canada
Bibliographic record
Abstract
Many communities along rivers in the Northwest Territories do not have water-level gauges, making flood hazard analyses difficult at these sites. These include the communities of Jean Marie River, Tulita and Fort Good Hope on the Mackenzie River, Nahanni Butte on the Liard River and Fort McPherson on the Peel River. However, gauges do exist at other sites upstream and downstream of these communities, from which flood hazard assessments can be extrapolated to the ungauged communities. Reach-based extrapolation becomes particularly challenging when analysing ice-jam flood hazards since data sparsity is an additional challenge at these locations. A simple empirical approach using non-dimensional stage and discharge was implemented, which allowed only a minimum of the required data from all sites to be extracted. From the gauged sites, water-surface elevations and slopes from digital elevation models, channel widths, thalweg elevations and ice thicknesses from under-ice flow measurement surveys and recorded water levels were obtained. As a test case, results from the gauged reach of Fort Simpson were extrapolated to the ungauged reach of Jean Marie River and are presented in this technical note.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".