Biological bank protection: trees are more effective than grasses at resisting erosion from major river floods
Bibliographic record
Abstract
Abstract Although it is recognized that streamside vegetation can reduce river bank erosion, the relative effectiveness of forest versus grassland has been unclear. To compare erosion resistance of the two vegetation types, we studied the free‐flowing Elk River in British Columbia, Canada from 1993 to 2014, including major floods in June 1995 and 2013. Interpretation of aerial photographs from 1994 and 2000 were used to examine the correspondence between floodplain vegetation and the extent of channel change after the 1995 flood. Along a 23 km reach with alternating forest and grassland, 15 locations displayed substantial change as the river moved a channel width (45 m) or more with meander migration, or up to 200 m with channel avulsion. All ten locations with major change (>75 m) occurred where the floodplain zones were occupied by grasslands, sometimes with small shrubs. In contrast, channels flanked by forest were minimally altered (<15 m), and deciduous (black cottonwood,Populus trichocarpa) or mixed deciduous‐coniferous groves were effective at resisting erosion. Some changes accompanied the 1995 flood and further changes followed as the destabilized banks were vulnerable to smaller floods in 1996 and 1997. Providing another comparison, a position that was dramatically scoured in 1995 when it was grassland had subsequent cottonwood colonization, and the 4 m trees resisted erosion from the 2013 flood. Thus, trees were more resistant than grassland to flood‐associated bank erosion. We recommend that riparian forests should be conserved to provide bank stability and to maintain an equilibrium of river and floodplain dynamics. Copyright © 2014 John Wiley & Sons, Ltd.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".