Floodplain stratigraphy of the ice jam dominated middle Yukon River: a new approach to long‐term flood frequency
Bibliographic record
Abstract
Abstract Floodplain stratigraphy is used as a new method for reconstructing ice jam flood histories of northern rivers. The method, based on reconstruction of the sedimentary record of vertically‐accreting floodplains, relies on stratigraphic logging and interpretation of floodplain sediments, which result from successive ice jam floods, and radiocarbon dating of inter‐flood organic material for chronology. In a case study along a reach of the Yukon River that straddles the Yukon–Alaska border, the method is used to develop a record of ice jam flooding for the last 2000 years. Detailed chronostratigraphic logs from three sites along the Yukon River indicates that the long‐term recurrence interval varies depending on location, but ranges from approximately once in 25 years to once in 38 years (or a probability of ca 3–4% in any given year). This is broadly similar to the 4·5% probability of recurrence calculated from archival and gauged data at Dawson City, Yukon Territory, for the period 1898–2006. Two of the three study locations, with sufficient chronology, suggest a decrease in flood frequency in the last several hundred years relative to the preceding period at each site, broadly corresponding to the Little Ice Age, suggesting climate exerts some control over long‐term ice jam flood frequency. This study demonstrates that the floodplain sedimentary record offers the potential to extend records of ice jam flooding in remote, ungauged northern rivers and provides a broader temporal context for assessing the frequency and variability of ice jam flooding. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| 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".