Quantitative relationships between river and channel-belt planform patterns
Bibliographic record
Abstract
Abstract Channel planform patterns arise from internal dynamics of sediment transport and fluid flow in rivers and are affected by external controls such as valley confinement. Understanding whether these channel patterns are preserved in the rock record has critical implications for our ability to constrain past environmental conditions. Rivers are preserved as channel belts, which are one of the most ubiquitous and accessible parts of the sedimentary record, yet the relationship between river and channel-belt planform patterns remains unquantified. We analyzed planform patterns of rivers and channel belts from 30 systems globally. Channel patterns were classified using a graph theory-based metric, the Entropic Braided Index (eBI), which quantifies the number of river channels by considering the partitioning of water and sediment discharge. We find that, after normalizing by river size, channel-belt width and wavelength, amplitude, and curvature of the belt edges decrease with increasing river channel number (eBI). Active flow in single-channel rivers occupies as little as 1% of the channel belt, while in multichannel rivers it can occupy >50% of the channel belt. Moreover, we find that channel patterns lie along a continuum of channel numbers. Our findings have implications for studies on river and floodplain interaction, storage timescales of floodplain sediment, and paleoenvironmental reconstruction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".