Modeling wood dynamics, jam formation, and sediment storage in a gravel‐bed stream
Bibliographic record
Abstract
In small and intermediate sized streams, the interaction between wood and bed material transport often determines the nature of the physical habitat, which in turn influences the health of the stream's ecosystem. We present a stochastic model that can be used to simulate the effects on physical habitat of forest fires, climate change, and other environmental disturbances that alter wood recruitment. The model predicts large wood (LW) loads in a stream as well as the volume of sediment stored by the wood; while it is parameterized to describe gravel bed streams similar to a well‐studied field prototype, Fishtrap Creek, British Columbia, it can be calibrated to other systems as well. In the model, LW pieces are produced and modified over time as a result of random tree‐fall, LW breakage, LW movement, and piece interaction to form LW jams. Each LW piece traps a portion of the annual bed material transport entering the reach and releases the stored sediment when the LW piece is entrained and moved. The equations governing sediment storage are based on a set of flume experiments also scaled to the field prototype. The model predicts wood loads ranging from 70 m 3 /ha to more than 300 m 3 /ha, with a mean value of 178 m 3 /ha: both the range and the mean value are consistent with field data from streams with similar riparian forest types and climate. The model also predicts an LW jam spacing that is consistent with field data. Furthermore, our modeling results demonstrate that the high spatial and temporal variability in sediment storage, sediment transport, and channel morphology associated with LW‐dominated streams occurs only when LW pieces interact and form jams. Model runs that do not include jam formation are much less variable. These results suggest that river restoration efforts using engineered LW pieces that are fixed in place and not permitted to interact will be less successful at restoring the geomorphic processes responsible for producing diverse, productive physical habitats than efforts using LW pieces that are free to move, interact, and form LW jams.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".