Urbanized stream restoration as designed adaptation: Planning using synthetic digital rivers and form variation assessment
Bibliographic record
Abstract
Abstract Restoring urbanized streams is challenging because of the absence of natural reference reaches and major changes in discharge and sediment supply as a consequence of catchment urbanization. In many cases the risk to urban infrastructure leads to form‐based restoration with less opportunity for process‐based approaches. Restoration involves designed adaptation to the new catchment state, rather than true restoration, but more‐natural designs are still feasible based in principles of fluvial geomorphology. An important element of this designed adaptation is planform design which is a part of urban channel restoration that is often simplified and which, in turn, simplifies the in‐stream morphology and habitats. More variable in‐stream morphology develops in irregular planforms which are also more common in nature. Tools and methods for designing complex planforms and associated bed morphology have developed, but further innovation is needed to meet interdisciplinary objectives in the planning phase of design. Here, we present a flexible approach for assessing surface‐form complexity of planform and in‐channel morphology as part of a move to geomorphically‐based urban riverscape design to accommodate urbanized channel changes within the overall interdisciplinary framework and to increase overall riverscape amenity. Our approach uses River Builder software for deriving 3D rivers based on hydro‐morphodynamics and applies the Geomorphic Form Variation (GFV) approach as a measure of geomorphic complexity for collaborative urbanized stream planning. Assessment of planform variable inputs showed a strong control on in‐channel morphology, and GFV showed that form complexity increases with higher width variability, stream bank roughness, and meander bend frequencies and curvatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".