Sediment Source Partitioning and Budgeting Over Historical Timescales in a Glacierized, Mountain Catchment
Bibliographic record
Abstract
Abstract Managing and living with geohazards is especially challenging in mountain landscapes and requires an understanding of catchment‐scale sediment dynamics and internal system functioning. While sediment budgeting is a valuable framework, challenges remain including partitioning sediment yield by source and grain size and addressing scale issues. This study advances our understanding of bed material dynamics in glacierized mountain catchments by applying a range of complementary techniques to measure sediment transfers in the Fitzsimmons Creek watershed. First, we measured the historical bed material yield using field surveys and historical air photo analysis, revealing an average specific sediment yield of 210 Mg km −2 yr −1 , that varied by a factor of 17 over the 76‐year record. Hydro‐meteorological and historical analyses suggest that gravel extraction had the largest impact over the past three decades, while an extreme landslide and flood event produced the highest recorded sediment yield. Second, we constructed a detailed sediment budget along the river system using high‐resolution, multi‐temporal lidar and geomorphic mapping data. Sediment source partitioning indicates that landslide, active channel, and floodplain sources each contributed about one‐third of the total sediment supply. Net degradation occurred along the valley bottom upstream of the fan‐delta, resulting in steadily increasing downstream sediment yield. This trend is punctuated by chronic landsliding near the outlet, driven by postglacial incision through glaciogenic sediments at a hanging valley step. Contemporary glacial and proglacial sources were not measured directly but surprisingly contributed minimally. These findings provide insight into the sediment dynamics of glacierized mountain catchments and their potential controls.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".