Spatial Patterns of Disconnectivity Explain Catchment‐Scale Sediment Dynamics and Transfer Efficiencies
Bibliographic record
Abstract
Abstract While connectivity studies are becoming common in the Earth sciences, disconnectivity has received much less attention. Sediment storage is the direct result of sediment disconnectivity and can provide concrete evidence of the spatial patterns of disconnectivity at the catchment‐scale. In this study, we explore the catchment‐scale sediment dynamics of the Tahoma Creek watershed, a high‐gradient glacio‐volcanic landscape, within a sediment budget framework and identify and map sources of disconnectivity to determine whether they explain the spatial patterns and estimated efficiencies of sediment transfers. We found that up to 80% of the total eroded sediment is sourced from the proglacial zone. The proglacial zone is characterized by high connectivity resulting from frequent debris flows and floods, and rapidly responds to changing conditions. Down valley, however, sources of disconnectivity become increasingly more prevalent, the hillslopes become decoupled from the channel, and a majority of the eroded sediment is redeposited with as little as ∼15% reaching the outlet. The spatial distribution of sources of disconnectivity and their upslope affected areas explains, to a large degree, catchment‐scale sediment dynamics and sediment transfer efficiencies and is in close agreement with quantitative connectivity estimates. We find that steep, glaciated watersheds are predominantly disconnected over human timescales and suggest that disconnectivity is the dominant state of landscapes over most timescales of interest. Mapping sources of disconnectivity provides a straightforward and concrete approach to estimating system disconnectivity and can increase confidence when paired with quantitative indices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".