Bibliographic record
Abstract
Abstract A new bank stability analysis procedure is developed for use in rational regime models predicting reach average channel dimensions. The performance of a regime model using this new bank stability formulation is compared against that for a model using the modified friction angle approach proposed by Millar and Quick (1993). The bank stability assessment is based on a conceptual model that more closely represents conditions found in gravel bed rivers with vegetated floodplains: the primary effect of vegetation is its contribution to a stable upper bank, the position of which is determined by erosion of unvegetated bed material at the toe of the bank. The vertical height of the upper bank is estimated using a simple slab failure model and assigning an effective cohesion to the vegetation‐reinforced soil. The geometry of the lower slope and the width of the channel are determined iteratively using the regime approach described by Eaton et al. (2004). A comparison of the predicted stream channel widths for stable gravel bed channels classified according to riparian vegetation type (Hey and Thorne, 1986) showed that this new formulation increases model accuracy, especially for the more densely vegetated channel types. Since the strength parameters used in the model can be estimated from the observed bank geometry, the potential for applying and testing rational regime models in the field has been significantly improved. Copyright © 2006 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".