Annual and decadal net morphological displacement of a small gravel‐bed channel
Bibliographic record
Abstract
Abstract The sediment supplied to a stream channel impacts the morphological trends experienced by that channel, and the long‐term trends are important to understand for many riverine applications. We introduce the term ‘net morphological displacement’ (NMD) to denote the net channel change revealed by the morphological method over multiple sediment transport events to make the concept more explicit for river management and use it to determine equilibrium or disequilibrium states. This study explores the morphological response of East Creek, a small threshold gravel‐bed channel in the Coast Mountains of British Columbia, Canada, to variations in flow and sediment supply at multiple spatial and temporal scales over 15 years. High‐resolution topographic data (HRTD) of the bed were combined with cross‐sectional surveys of the banks to determine the sediment supply that the channel responded to annually. The level of detection used to remove noise associated with HRTD was calibrated using an independent tracer stone dataset. The net effects from multiple floods caused distributions of bed elevation change to generally follow the log‐normal distribution, and mean depths of erosion and deposition were predominantly similar between morphological units. At the reach scale, the various reaches of East Creek responded differently to the same hydrological events due to the impacts from the varying supply conditions on the NMD. Shorter measurement periods would have resulted in inconclusive information that does not show the long‐term morphological trends of the channel. Determination of these trends can take years or decades, depending on the time and space scales of change, but there is generally a lack of long‐term channel monitoring programmes, notably after river restoration. More long‐term channel monitoring programmes are required to assess restoration projects and ensure their long‐term sustainability.
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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 | 0.000 | 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 | 0.000 | 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".