Linking connectivity to spatiotemporal variability in sediment dynamics and yield in glacierized, mountainous watersheds
Bibliographic record
Abstract
An understanding of catchment-scale processes and sediment dynamics is crucial for the informed and sustainable development of mountain communities. Given the steep topography, glacier retreat, and intensifying weather patterns due to climate change, many mountain towns face heightened vulnerability to geohazards. Studies show that as glaciers retreat, paraglacial processes typically lead to elevated sediment yields, exacerbating existing hazards. However, postglacial landscapes are dynamic, complex, and heterogeneous systems shaped by a variety of processes, and no two systems are the same. The efficiency in which glacial sediments are reworked and transported to and through river systems (connectivity) varies over time and space. In this study, we investigate the link between landscape history, sediment (dis)connectivity, and postglacial sediment dynamics in a glacierized, mountainous catchment in Southern British Columbia. We begin by mapping the geomorphology, identifying sediment sources, storage landforms and transfer processes. Subsequently we employ morphometric analysis and landform mapping paired with age estimates, to quantify sediment yield. These results are compared to historical channel changes and estimates of structural connectivity to better understand the variation in postglacial sediment dynamics. By integrating diverse datasets and methodologies, we are able to estimate the variability in sediment yield and changing relative contributions of sediment sources at a range of spatial and temporal scales. Preliminary results of this work shed light on and underscore the need for additional studies that investigate long-term (e.g., postglacial) changes in sediment connectivity. Such research can inform decision-making in landscapes that are rapidly changing and experiencing deglaciation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".