Can we distinguish flood frequency and magnitude in the sedimentological record of rivers?
Bibliographic record
Abstract
Consideration of the origin of alluvial deposits and their paleoenvironmental interpretation has traditionally involved two schools of thought: that they are either the result of processes that, on average, have acted uniformly through time, or that they are related to exceptional events that occur infrequently. Despite the long-running debate of gradualism versus catastrophism within the Earth Sciences, there are surprisingly few quantitative data to assess the magnitude of events that produce alluvial sedimentary successions. This paper reports on a unique ‘natural experiment’ where surface (digital elevation model, DEM) and subsurface (ground penetrating radar, GPR) data were taken immediately prior to, and after, a large (1-in-40 yr) flood event on the sandy, braided, South Saskatchewan River, Canada. Results show that although this high-magnitude flood reworked the entire braidplain, the scale of scour and style of deposition was similar to that associated with lower-magnitude, annual, floods. The absence of a distinct imprint of this large flood within the deposits is related to the fact that as river discharge rises, and begins to flow overbank, channel width increases at a much faster rate than flow depth, and thus the rate of increase in channel bed shear stress declines. Hence, rather than being a product of either frequent or rare events, alluvial deposits will likely be created by a range of different magnitude floods, but discriminating between these different scale events in the rock record may be extremely difficult.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".