Bibliographic record
Abstract
Landscapes are shaped by the interaction of tectonics, climate, and rock erosion dynamics. Active incision in bedrock rivers sets the pace of landscape evolution because river incision cuts deep valleys and canyons into bedrock, transporting that material to the sea. This unburdens Earth's surface, allowing uplift of majestic mountain peaks in tectonically active settings. Bedrock-bound rivers, where the banks and bed are mostly bedrock, are hard points in the landscape that set the upstream base level of drainage basins and that must be vertically incised to lower landscape elevation and balance erosion against tectonic uplift. There are four distinct bedrock-bound channel morphologies that do not occur in alluvial channels—constriction-pool-widenings, rapids, overfalls, and waterfalls—each of which has a distinct flow structure. Our ability to predict bedrock-bound channel morphodynamics is nascent, but the discovery of mechanistic lateral bedrock erosion models, coupled with existing vertical incision models, allows prediction of bedrock river geometry and adjustments due to changes in water flux, sediment supply, and regional uplift. ▪ Coupled lateral and vertical erosion models reveal that the geometry of bedrock rivers is dominantly controlled by sediment supply, not discharge. ▪ Coupling observations of nonuniform flow structures and erosion models confirm that bedrock-bound channels are loci of intense erosion along a river's profile. ▪ Prediction of the 3D shape of bedrock-bound rivers is possible by combining models for flow, sediment transport, and bedrock erosion. ▪ Morphodynamic predictions are limited by poor understanding of nonuniform flow structures, flow resistance, and sediment transport in bedrock-bound channels.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".