Predicting gravel bed river response to environmental change: the strengths and limitations of a regime‐based approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it