A 1300 year reconstruction of paleofloods using oxbow lake sediments in temperate southwestern Quebec, Canada
Bibliographic record
Abstract
The study of paleofloods provides important information on past flood frequency and intensity for regions where there is a paucity of records; it therefore extends our knowledge of flood occurrence beyond the historical record. Many paleoflood reconstructions come from the arid dry climate of southwestern USA and from Europe, with few studies being conducted in temperate climates of North America. This study uses sediment cores from oxbow lakes to reconstruct past flood events in a temperate region. Cores extracted from two oxbow lakes along the Désert River in southwestern Quebec, Canada, were analyzed for magnetic susceptibility, loss on ignition, and grain size and were radiocarbon dated (14C). Using a combination of magnetic susceptibility variations, along with changes in grain size and organic material content, five floods were identified within the 220 cm core (1300 years) from the North oxbow lake, and six floods in the 118 cm core (600 years) from the South oxbow lake. This study provides evidence to support the use of oxbow lakes in temperate regions as a proxy of past floods, thus helping us understand hydroclimatic changes at regional scales. Data that span a longer period of time and in different environments are key to increase flood modelling accuracy to improve mitigation strategies under a changing climate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".