Fluvial aggradation in Vedder River: Testing a one‐dimensional sedimentation model
Bibliographic record
Abstract
Sediment routing models which simulate the coevolution of river long profile and bed grain size distributions have been used to investigate downstream fining above base level, channel response to rectification, and other disequilibrium situations at a variety of timescales. We extend one such model (SEDROUT [Hoey and Ferguson, 1994]) to deal with sand as well as gravel and test its ability to simulate aggradation and downstream fining in a well‐documented Canadian river. Previous tests of this and similar models have been largely restricted to comparing observed and simulated gradients of downstream fining, but it is not obvious when to make the comparison in a time‐dependent model with uncertain initial state and no equilibrium except in the very long term. We discuss and apply a more rigorous set of test criteria and address issues of defining initial conditions and time to test. The model's varying sensitivity to different boundary conditions and parameters indicates key data constraints on the testability and predictive accuracy of any such model. We also consider the adequacy of one‐dimensional calculations in channels with variable width and present initial results of attempts to allow for this.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".