Scale-dependent interactions between wood and channel dynamics: Modeling jam formation and sediment storage in gravel-bed streams
Bibliographic record
Abstract
[1] A stochastic model is used to investigate how the geomorphic function of wood changes with watershed scale, assuming wood recruitment occurs due to the mortality of individual trees, not to mass recruitment events such as landslides or episodic bank erosion. The model replicates the downstream decline in total wood load observed in the field, but predicts that the functional wood load peaks in channels having bankfull widths about 33% of the characteristic riparian tree height. The model also predicts that the greatest potential impact of jams on channel pattern—both in terms of sediment stored behind individual jams and the potential for jams to trigger avulsions—will typically be associated with channel widths between 25% and 67% of the riparian tree height. The simulation results are used to refine the categories that describe wood in alluvial channels, and the equivalent terms that describe the size of streams with forested riparian areas: small channels (or channels with large wood) are associated with widths less than 25% of the tree height; large channels (or channels with small wood) are associated with widths greater than 67% of tree height; and medium channels (or channels with intermediate wood) have widths between 25% and 67% of the tree height. We surmise that large wood acts primarily to store bed material (in small channels); intermediate wood tends to form channel-spanning jams, which can induce channel avulsions and create anabranched channel patterns (in medium channels); and small wood may increase the morphologic diversity, but does not store significant quantities of bed material or form channel-spanning jams capable of inducing stream avulsions (in large channels).
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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".