Bibliographic record
Abstract
Earth's terrestrial topography evolves in response to the interaction of tectonics, climate, and lithology. Recent discoveries suggest that the grain size of sediments produced on hillslopes and transported through river networks is key to understanding these interactions. Hillslope grain size varies systematically with erosion rate and residence time, the degree of chemical and physical weathering, and the fracture density and susceptibility to weathering of rock. Variations in initial grain size strongly influence the spatial evolution of grain size distributions as particles mix and wear during downstream transport through channel networks. In rivers, the size and flux of the coarse fraction of the sediment load control the rate of incision into bedrock and thus govern channel slope and ultimately the relief of actively eroding landscapes. These relationships suggest that a primary way that tectonics, climate, and lithology influence landscape evolution is through their controls on sediment grain size. ▪ Recent research reveals the central role of sediment grain size in controlling bedrock river morphodynamics, linking grain size to channel slope and topographic relief. ▪ Tectonics, climate, and lithology govern the size of sediments produced on hillslopes; hence, grain size mediates their influence on landscape evolution. ▪ Feedbacks linking sediment grain size, topography, weathering, erosion, and sediment transport provide new opportunities for advances in Earth surface science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".