Bibliographic record
Abstract
Abstract. The streampower fluvial erosion (SP) model is the basis for many analyses and simulations of landscape evolution. It assumes that the rate of river incision into bedrock depends only on flow intensity and rock erodibility, and is insensitive to sediment flux. In two dimensions, the SP model is often coupled with diffusion processes, which together describe the coupled evolution of channels and hillslopes (SPD models). While it is implicitly assumed that channels in the SPD models retain their detachment-limited character, this has not been extensively tested. Here we show that the deposition component of hillslope diffusion has a substantial effect on channel slope and relief in SPD models, and present a new method to predict the channel steepness index from model parameters. We contrast the results with those of a mixed bedrock-alluvial river model coupled with a hillslope diffusion model that both track sediment mass balance, and suggest that the combination of mass-conservative hillslope processes and non-mass-conservative fluvial erosion in SPD models leads to unrealistic scaling behavior. We demonstrate this by examining several field sites where an SPD model adequately describes the spacing of first-order valleys, and show that it is inadequate to predict channel steepness.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.372 | 0.291 |
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".