Anthropogenic sediment traps and network dislocation in a lowland UK river
Bibliographic record
Abstract
Abstract Farm ponds, reservoirs and in‐stream weirs exist in most lowland UK river catchments and often dominate natural features such as lakes, wetlands, floodplains and debris dams. Artificial structures have served multiple purposes, including provision of power for historic flour milling and iron ore crushing and provision of water for medieval fishponds, canals, crop irrigation and potable supply. Although unintentional, they can significantly affect longitudinal connectivity, including sediment delivery pathways, through river catchments. We report results from three spatially nested case studies that were undertaken in the Rother catchment, ranging in scale from small farm ponds of a few square metres in area, to larger in‐stream weirs and reservoirs (locally called ponds). Reservoirs typically trap sediment, decreasing sediment availability downstream, while inducing valley sediment accumulation upstream. We focus on the quantity and particle size characteristics of sediment trapped behind these structures compared to catchment soils and to sediments that are transported through, and deposited in, ‘natural’ gravel‐bed reaches. At all scales our results demonstrate that sediment trapping and release are particle size specific. Fine to coarse sands (125 μm to 2 mm diameter) and coarser sediments are retained behind structures at all scales, while silts and clays (<63 μm diameter) and organic matter are generally depleted in the stored sediment. Even though 75% of the surveyed reservoirs have very low estimated trap efficiencies (<5%), they slowly fill over time with sediment. An important management question relates to the likely benefits of impoundment, structure or sediment removal, and whether fine (here defined as <63 μm) or coarser (>63 μm) sediment is a priority for management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".