The current state of the use of large wood in river restoration and management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Trees fall naturally into rivers generating flow heterogeneity, inducing geomorphological features, and creating habitats for biota. Wood is increasingly used in restoration projects and the potential of wood acting as leaky barriers to deliver natural flood management by ‘slowing the flow’ is recognised. However, wood in rivers can pose a risk to infrastructure and locally increase flood hazards. The aim of this paper is to provide an up‐to‐date summary of the benefits and risks associated with using wood to promote geomorphological processes to restore and manage rivers. This summary was developed through a workshop that brought together academics, river managers, restoration practitioners and consultants in the UK to share science and best practice on wood in rivers. A consensus was developed on four key issues: (i) hydrogeomorphological effects, (ii) current use in restoration and management, (iii) uncertainties and risks and (iv) tools and guidance required to inform process‐based restoration and management.
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it