Riparian forest structure and stream geomorphic condition: implications for flood resilience
Bibliographic record
Abstract
Managing riparian corridors for flood resilience requires understanding of linkages between vegetation condition and stream geomorphology. Stream assessment approaches increasingly use channel morphology as an indicator of stream condition, with only cursory examination of riparian vegetation. Our research (i) examines relationships between stream geomorphic condition, as assessed by Rapid Geomorphic Assessment (RGA) scores, and riparian forest structure, and (ii) investigates scale dependencies in the linkages between land cover and stream geomorphology. We sampled vegetation structure and composition and assessed geomorphic condition at 32 stream reaches within the Lake Champlain Basin, USA. RGA scores were modeled as a function of structural attributes using classification and regression trees. Landsat coverages were used to delineate land uses within five nested spatial scales. Generalized linear models (GLM) evaluated relationships between land cover and RGA scores. Standard deviation of basal area partitioned the greatest variability in RGA scores, but dead tree density and basal area (positively) and shrub density (negatively) were also significant predictors. RGA was related to forest and agricultural cover at the two finest scales. Riparian forest structure is highly dynamic in relation to stand development and disturbance history; simple forest cover information does not capture these differences or their influences on stream geomorphic condition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".