Large wood transport and jam formation in a series of flume experiments
Bibliographic record
Abstract
Abstract Large wood has historically been removed from streams, resulting in the depletion of in‐stream wood in waterways worldwide. As wood increases morphological and hydraulic complexity, the addition of large wood is commonly employed as a means to rehabilitate in‐stream habitat. At present, however, the scientific understanding of wood mobilization and transport is incomplete. This paper presents results from a series of four flume experiments in which wood was added to a reach to investigate the piece and reach characteristics that determine wood stability and transport, as well as the time scale required for newly recruited wood to self‐organize into stable jams. Our results show that wood transitions from a randomly distributed newly recruited state to a self‐organized, or jam‐stabilized state, over the course of a single bankfull flow event. Statistical analyses of piece mobility during this transitional period indicate that piece irregularities, especially rootwads, dictate the stability of individual wood pieces; rootwad presence or absence accounts for up to 80% of the variance explained by linear regression models for transport distance. Furthermore, small pieces containing rootwads are especially stable. Large ramped pieces provide nuclei for the formation of persistent wood jams, and the frequency of these pieces in the reach impacts the travel distance of mobile wood. This research shows that the simulation of realistic wood dynamics is possible using a simplified physical model, and also has management implications, as it suggests that randomly added wood may organize into persistent, stable jams, and characterizes the time scale for this transition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".