Flood events and flood risk assessment in relation to climate and land-use changes: Saint-François River, southern Québec, Canada
Bibliographic record
Abstract
In the current context of climatic variability, it is important to quantify the impact on the environment. This study deals with an analysis of climatic data and land-use changes in terms of the impacts on flood recurrence based on multisource data. The study area covers the mouth of the Saint-François River (southern Québec, Canada), where spring floods and ice jams are a recurring problem. The flood frequency analysis shows an increase in flooding over recent decades, attributable to an increase in winter temperatures that has the effect of causing ice jams earlier in the year. Regarding land-use changes, a small decrease in agricultural surface areas is observed, from 53% to 39%, along with increases in forest and urban surface areas from 27% to 38% (forest) and 3% to 5% (urban) between 1928 and 2005. In a context of continuing climate warming, more pronounced inter-annual variations are to be expected along with a higher incidence of flooding. Editor Z.W. Kundzewicz Citation Ouellet, C., Saint-Laurent, D. and Normand, F., 2012. Flood events and flood risk assessment in relation to climate and land-use changes: Saint-François River, southern Québec, Canada. Hydrological Sciences Journal, 57 (2), 313–325.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".