Predicting gravel bed river response to environmental change: the strengths and limitations of a regime‐based approach
Bibliographic record
Abstract
Abstract Rivers respond to environmental changes such as climate shifts, land use changes and the construction of hydro‐power dams in a variety of ways. Often there are multiple potential responses to any given change. Traditionally, potential stream channel response has been assessed using simple, qualitative frameworks based largely on professional judgement and field experience, or using some form of regime theory. Regime theory represents an attempt to use a physically based approach to predict the configuration of stable channels that can transport the imposed sediment supply with the available discharge. We review the development of regime theory, and then present a specific regime model that we have created as a stand‐alone computer program, called the UBC Regime Model (UBCRM). UBCRM differs from other regime models in that it constrains its predictions using a bank stability criterion, as well as a pattern stability criterion; it predicts both the stable channel cross‐sectional dimensions as well as the number of anabranches that the stream must have in order to establish a stable channel pattern. UBCRM also differs from other models in that it can be used in a stochastic modelling mode that translates uncertainty in the input variables into uncertainty in the predicted channel characteristics. However, since regime models are fundamentally based on the concept of grade, there are circumstances in which the model does not perform well. We explore the strengths and weaknesses of the UBCRM in this paper, and we attempt to illustrate how the UBCRM can be used to augment the existing qualitative frameworks, and to help guide professionals in their assessments. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".