Tributary‐junction fans as buffers in the sediment cascade: a multi‐decadal study
Bibliographic record
Abstract
Abstract Alluvial fans at tributary junctions modulate sediment flux through river networks, by buffering the mainstem channel from disturbance in the tributaries. Buffering occurs through the storage (and release) of sediment in fans. Here, we use an extensive historic dataset to characterise the ways in which fan buffering can change as sediment supply varies. In New Zealand's East Coast region, sediment supply and fluvial transport are prolific by global standards. We reconstruct how tributary‐junction fans in this region have responded to sediment generated by deforestation and extreme storms. The dynamics of five fans along the Tapuaeroa River are examined for the period 1939–2015. In response to major sediment loading, fans aggraded by up to 12 m and prograded by up to 170 m. Net sediment accumulation ranged from near zero to 1.5×106 m3. Fan size, gradient, sediment storage and buffering were influenced by both upstream and downstream controls. Key upstream (tributary) influences were sediment supply and stream power; downstream (mainstem) influences included distal confinement and, importantly, the nature of fan interaction with the mainstem, which aggraded by up to 6 m. The fans' ability to buffer the Tapuaeroa River from change in the tributaries was largely governed by this downstream interaction: as the mainstem aggraded, it increasingly curtailed fan progradation, thus limiting buffering. Previous studies of tributary‐junction fans have related fan morphometry to basin characteristics. However, we find that fan slope and area can vary considerably at decadal, annual or even monthly timescales. Consequently, we suggest that such studies could benefit by examining regional histories of disturbance. © 2019 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| 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".