Biotic forcing militates against river meandering in the modern Bonneville Basin of Utah
Bibliographic record
Abstract
Abstract Biotic forcing on river meandering is a highly debated topic in sedimentology. Vegetation is assumed to hold a vital role on channel stability and sinuosity, for example through bank stabilization and pedogenic production of cohesive clays. However, statistically solid and causal relationships between vegetation density and river sinuosity remain largely untested in natural systems. This study investigates physical and biotic forcings on channel sinuosity in the Bonneville Basin of Utah (USA), an endorheic depression flanked by active fluvial networks (‘washes’) that display diverse vegetation density and channel‐planform style. By means of remote sensing and ground‐data collection, 58 washes are considered, 0·1 to 90 km 2 in surface area and drained by trunk channels <45 m wide and <1·2 m deep. Each wash is composed of a catchment basin connected downstream to an aggradational and distributive channel network. Statistically solid regressions highlight the primary roles played by base level and catchment size on fluvial morphogenesis. In contrast, no correlation is found between vegetation density and other parameters such as trunk‐channel width or surface area of the largest meander in a wash. Similarly, no statistical correlation exists between vegetation density and meander size or sinuosity index. Rather, larger and more sinuous meanders are invariably associated with lower vegetation density. These results are corroborated by field evidence showing that sparse vegetation promotes flow disturbance, channel branching and bar braiding instead of stabilizing sediment surfaces. Thus, river meandering is attributed to cohesion offered by mud retention within the endorheic basin, as well as discharge and stream‐power modulation along bifurcating and low‐gradient channel reaches. Hence, this work demonstrates how meandering‐channel patterns may arise from entirely physical forcings in the absence of vegetation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".