Comparison of flood index characteristics and channel morphology of two Canadian Shield rivers based on wetland area in southern Quebec (Canada)
Bibliographic record
Abstract
Two characteristics (magnitude and duration-frequency) of two indices of high (Qmax, annual flooding) and low intensities (Q90) floods and the morphology (bankfull width and sinuosity) of channels were compared for the Matawin (1,390 km²) and Petite Nation (1,330 km²) rivers on the Canadian Shield, which are differentiated mainly by wetland and forest areas. Wetlands cover 9% and 15% respectively in the Matawin and Petite Nation watersheds. This comparison revealed that the magnitude of high floods (Qmax) in the Matawin River was on average about twice the magnitude of high floods in the Petite Nation River from 1945–2019. No significant difference was observed in the magnitude of low floods (Q90) between the two rivers. As for the duration-frequency of heavy floods (Qmax), it was, on average, about three times higher in the Petite Nation River watershed than in the Matawin River watershed. The opposite is true for low floods (Q90). Morphologically, this difference in the magnitude and duration-frequency of heavy floods is seen in the bankfull width and sinuosity between the channels of the two rivers. The Matawin River channel is narrower and more sinuous than the Petite Nation River channel. This study is the first to demonstrate the impact of wetlands on channel morphology for Canadian Shield rivers in southern Quebec.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".