Variable hillslope‐channel coupling and channel characteristics of forested mountain streams in glaciated landscapes
Bibliographic record
Abstract
Abstract Channel morphology of forested, mountain streams in glaciated landscapes is regulated by a complex suite of processes, and remains difficult to predict. Here, we analyze models of channel geometry against a comprehensive field dataset collected in two previously glaciated basins in Haida Gwaii, B.C., to explore the influence of variable hillslope–channel coupling imposed by the glacial legacy on channel form. Our objective is to better understand the relation between hillslope–channel coupling and stream character within glaciated basins. We find that the glacial legacy on landscape structure is characterized by relatively large spatial variation in hillslope–channel coupling. Spatial differences in coupling influence the frequency and magnitude of coarse sediment and woody material delivery to the channel network. Analyses using a model for channel gradient and multiple models for width and depth show that hillslope–channel coupling and high wood loading induce deviations from standard downstream predictions for all three variables in the study basins. Examination of model residuals using Boosted Regression Trees and nine additional channel variables indicates that ~10 to ~40% of residual variance can be explained by logjam variables, ~15–40% by the degree of hillslope–channel coupling, and 10–20% by proximity to slope failures. These results indicate that channel classification systems incorporating hillslope–channel coupling, and, indirectly, the catchment glacial legacy, may present a more complete understanding of mountain channels. From these results, we propose a conceptual framework which describes the linkages between landscape history, hillslope–channel coupling, and channel form. © 2018 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".