Flood-Drought Hazard Assessment for a Flat Clayey Deposit in the Canadian Prairies
Bibliographic record
Abstract
Dry climate, clayey soil, and flat topography govern water balance in the southern part of the Canadian Praries. The main purpose of this work was to assess flood-drought hazard using Regina as a typical urban centre in the region. Results indicate that extreme weather patterns are frequent and meteorological parameters have changed from 1970 to 2015: precipitation (+50 mm), air temperature (+0.9oC), relative humidity (+6%), wind speed (-1.35 km/hr), and solar radiation (+0.9 MJ/m2). In the dry climate (Dfb), 77% of the total annual precipitation (386 mm/year) occurs from April to September. The runoff coefficient of 0.6 relates to 63% impervious areas (commercial, industrial and residential) and 35% near-impervious areas (open spaces with low hydraulic conductivity). The flat topography (570 m through 600 m asl over 124 km2) along with a low channel slope of up to 0.4% results in water ponding during short-term and high-intensity rainfalls. Water is managed through the Wascana Creek that holds 98% of the total water volume (84 x 106 m3) in the city. From April to September, volume fluctuations depend on antecedent water levels and meteorological conditions. The city has recently received several events of flash floods (2010 and 2014) and long-term droughts (1984 and 2017). The negative average change in storage indicates drought-like conditions during spring-summer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".