Grazing impacts on gully dynamics indicate approaches for gully erosion control in northeast Australia
Bibliographic record
Abstract
Abstract Drainage network extension in semi‐arid rangelands has contributed to a large increase in the amount of fine sediment delivered to the coastal lagoon of the Great Barrier Reef, but gully erosion rates and dynamics are poorly understood. This study monitored annual erosion, deposition and vegetation cover in six gullies for 13 years, in granite‐derived soils of the tropical Burdekin River basin. We also monitored a further 11 gullies in three nearby catchments for 4 years to investigate the effects of grazing intensity. Under livestock grazing, the long‐term fine sediment yield from the planform area of gullies was 6.1 t ha ‐1 yr ‐1 . This was 7.3 times the catchment sediment yield, indicating that gullies were erosion hotspots within the catchment. It was estimated that gully erosion supplied between 29 and 44% of catchment sediment yield from 4.5% of catchment area, of which 85% was derived from gully wall erosion. Under long‐term livestock exclusion gully sediment yields were 77% lower than those of grazed gullies due to smaller gully extent, and lower erosion rates especially on gully walls. Gully wall erosion will continue to be a major landscape sediment source that is sensitive to grazing pressure, long after gully length and depth have stabilised. Wall erosion was generally lower at higher levels of wall vegetation cover, suggesting that yield could be reduced by increasing cover. Annual variations in gully head erosion and net sediment yield were strongly dependent on annual rainfall and runoff, suggesting that sediment yield would also be reduced if surface runoff could be reduced. Deposition occurred in the downstream valley segments of most gullies. This study concludes that reducing livestock grazing pressure within and around gullies in hillslope drainage lines is a primary method of gully erosion control, which could deliver substantial reductions in sediment yield. Copyright © 2018 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".