Land use and landscape position influence soil organic phosphorus speciation in a mixed land use watershed
Bibliographic record
Abstract
Abstract Land use can significantly alter soil P forms, which will influence P loss in runoff. Organic P (Po) compounds are an important component of soil P, but their forms and cycling in soils with different land uses are still poorly understood. In addition, streambanks are potential sources of P loss; P forms and concentrations in streambank soils may vary with land use, affecting potential P loss to water. This study used solution 31P nuclear magnetic resonance spectroscopy to characterize and quantify P in interior and streambank soils (0–10 cm) under duplicate sites from four different land uses along streams in the Missisquoi River basin (VT, USA): silage corn, hay meadow, emergent wetlands, and forest. Orthophosphate monoesters were the dominant P compound class regardless of land use or landscape position. Forest soils had the lowest Po concentrations, less labile P forms than other soils, and significantly lower concentrations of total inositol hexakisphosphates and total orthophosphate monoesters compared with corn soils. Riparian buffer zones for agricultural soils lowered P concentrations in streambank soils for many soil P pools relative to interior soils. The wetland soils of this study had P concentrations and P forms that were similar to those for interior agricultural soils and generally showed no reduction in P concentrations in streambank soils relative to interior soils. This is consistent with the role of wetlands as P sinks in the landscape but also suggests these wetlands should be carefully monitored to minimize P accumulation, especially in streambank soils.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".