Quantifying sediment connectivity: Moving toward a holistic assessment through a mixed methods approach
Bibliographic record
Abstract
Abstract The sediment connectivity concept is particularly suitable for tackling complex, multidisciplinary questions through systems thinking. However, it is unclear how to directly measure connectivity, and so proxy variables are often applied by indices to provide estimates. In this study, we critically evaluate a broad range of connectivity indices encompassing structural and functional connectivity as well as hillslope channel coupling. We then discuss their role in providing a more holistic estimation of connectivity within the Tahoma Creek Watershed, WA. Of the 14 km of channel below the glacier front, the upper 6 km (0–6 km) are coupled to the adjacent hillslopes. Coupled reaches correspond to regions with a high proportion of area contributing sediment and relatively high connectivity values, where all measures decrease in the downstream direction. A significant transition occurs near river kilometer 6. Here the valley bottom abruptly widens, deposition occurs, and the hillslopes become decoupled from the active channel. This transitional reach is also identified as a geomorphic hotspot based on the network structure. The lower 8 km of channel downstream of this reach are largely depositional and percent contributing area and connectivity values remain low, eventually reaching a minimum. Despite their limitations, we found each method to provide unique and useful information regarding connectivity. The effect of scale and event magnitude on connectivity is illustrated. We also found vegetation, topographic characteristics, and network structure to be important in high‐gradient glacio‐volcanic landscapes. The choice of an index will depend on the research objectives, data availability, and the proxy variables that best describe the variability within the defined area.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".