Time and Intensity Weighted Indices of Fluvial Processes: a Case Study from the Kootenai River, USA
Bibliographic record
Abstract
Abstract Within riparian landscapes, river flows and stages determine habitat gradients from less to more dynamic, and these support different plant species and their life history stages that are adapted to specific positions along these gradients. The gradients are characterized by physical processes that vary in magnitude and duration, and these shape the riparian vegetation communities. Consequently, natural riparian ecosystems are very dynamic, and the river disturbance regime is essential for sustaining ecosystem health. However, although the importance of disturbance is well accepted, disturbance regimes are poorly understood. This study was undertaken to develop indices capable of characterizing riparian habitats by considering flood magnitude and the elapsed time after flood disturbance, that is, the history that influenced the present vegetation composition. The indices were tested along two reaches of the Kootenai River in Idaho, USA, with braided versus meandering channel forms. The case study spanned a 31‐year period and emphasized two major disturbance components, the morphodynamic influence of velocity and shear stress and the flood or inundation duration. Computed indices were tested for consistency and then used to characterize different riparian vegetation development and succession phases. The statistical analysis revealed high correspondence among the calculated indices and differences across the different successional stages and between the two reaches. This demonstrated the utility of the time and intensity weighted indices to analyse the fluvial patterns that support different riparian vegetation communities, and this could be applicable for riparian management, mitigation, conservation and restoration. Copyright © 2016 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 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 | 0.002 |
| 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 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".