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 (P o ) 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 31 P 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 P o 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".