Evaluation of contrasting buffer features within an agricultural landscape for reducing sediment and sediment‐associated phosphorus delivery to surface waters
Bibliographic record
Abstract
Abstract There are a variety of buffering features within the landscape that can be used to trap sediment and associated contaminants such as phosphorus (P), thereby helping to reduce sediment and P delivery to watercourses. Astroturf mats ( n = 136) were placed within contrasting buffer features at nine sites [mid‐field hedges (two sites), edge‐of‐field grass strips (six sites) and channel wetlands (one site)] within the River Parrett basin in England. Sediment was recorded on the mats at seven of the sites during the 18‐month sampling period. At the other two sites either there was insufficient erosion or sediment by‐passed the mats. At the seven sites where mats collected sediment, there was a considerable range in sediment deposition over the 18‐month sampling period with site‐average values (based on all mats at a site) ranging from 0.02 ± 0.06 to 1.15 ± 1.88 g cm −2 ; the average for all 136 mats was 0.41 ± 1.08 g cm −2 , or approximately 0.27 g cm −2 year −1 . Most of the sediment collected on the mats ( n = 60) was sand‐sized (>63 μm) material. The site‐average total‐P content of the <63 μm fraction of the deposited sediment ranged between 616 and 1938 mg kg −1 (average 890 mg kg −1 ). About half of all the mats that collected sediment were from the front of the buffers. Comparison of the sediment in the buffer features with topsoil from the contributing upslope fields suggests that the buffers trap coarser sediment with lower P concentrations, than the contributing topsoil. This suggests that the finer fraction, enriched in total‐P, may be passing through the buffers towards river channels. Comparison between sites indicates that sediment deposition within buffers is greater at sites with steeper slopes, erodible soils and certain types of land use, such as maize for silage, reflecting the greater soil erosion and sediment transfers in these fields. The location and careful design of buffer features is a key factor in their effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".