Sediment source partitioning and budgeting over historical timescales in a glacierized, mountain catchment
Bibliographic record
Abstract
Managing and living with geohazards is especially challenging in mountain landscapes. Informed management relies on an understanding of catchment-scale sediment dynamics and system functioning. Sediment budgeting can be used as a framework and practical management tool to organize and analyze the necessary data. However, it can be challenging to constrain sediment budget components, partition sediment yield measurements by source and grain size, and resolve scale issues. In this study, we seek to better constrain the spatial and temporal patterns of bed material transfer by leveraging a suite of techniques to measure and quantify sediment transfers in a glacierized, mountain catchment. First, we quantify the historical bed material yield using field surveys and historical air photo analysis. Second, we utilize high-resolution, multi-temporal lidar data and detailed geomorphic mapping to construct a detailed sediment budget. A mixed-methods approach and careful uncertainty analysis was required to resolve the historical sediment yield and detailed sediment budgeting results. In the Fitzsimmons Creek Watershed, the annual sediment yield varied by up to a factor of 10 over the 76-year record. Sediment source partitioning suggests landslides, active channel, and floodplain sources each contributed 1/3 of the total sediment supply. Importantly, the landsliding occurs proximal to the outlet and transports glacial valley fill to the channel, significantly increasing sediment yield. Point-based sediment yield estimates are helpful for long-term evaluation of landscape denudation and system change, while detailed sediment budgets provide information necessary for understanding complex system functions and dynamics, and for management applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".