Evaluating river morphology change with a geomorphic form variation approach
Bibliographic record
Abstract
Abstract Geomorphic river design strives for natural resilience by encouraging geomorphic form complexity and morphological processes linked to greater habitat diversity. Increasing availability of high‐resolution topographic data and spatial feature mapping methods provide advantages for morphological analysis and river restoration planning. We propose and evaluate an approach to quantifying topographic variability of geomorphic form and pixel‐level surface roughness resulting from channel planform geometry differences using spatially continuous variety computation applied to component metrics including flow direction, aspect and planform curvature. We define this as the geomorphic form variation (GFV) approach and found it scalable, repeatable and a multi‐stage analytical metric for quantifying physical aspects of river‐bed topographic variability. GFV may complement process‐based morphological feature mapping applications, hydraulic assessment indices and spatial habitat heterogeneity metrics commonly used for ecological quality evaluation and river restoration. The GFV was tested on controlled synthetic channels derived from River Builder software and quasi‐controlled sinuous planform flume experiment channels. Component variety metrics respond independently to specific geometric surface changes and are sensitive to multi‐scaled morphology change, including coarser‐grained sediment distributions of pixel‐level surface roughness. GFV showed systematic patterns of change related to the effects of channel geometry, vertical bed feature (pool‐bar) frequency and amplitude, and bar size, shape and orientation. Hotspot analysis found that bar margins were major components of topographic complexity, whereas grain‐scale variety class maps further supported the multi‐stage analytical capability and scalability of the GFV approach. The GFV can provide an overall variety value that may support river restoration decision‐making and planning, particularly when geomorphic complexity enhancement is a design objective. Analysing metric variety values with statistically significant hotspot cluster maps and complementary process‐based software and mapping applications allows variety correspondence to systematic feature changes to be assessed, providing an analytical approach for river morphology change comparison, channel design and geomorphic process restoration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".