Influence of wetlands on nutrients in headwaters of agricultural catchments
Bibliographic record
Abstract
Abstract We investigated the influence of land cover on nutrient concentrations (295 samples) in headwater streams over a 2‐year period in 10 agriculture‐dominated subcatchments (163–8373 ha) in southern Ontario Canada. In this region, monitoring and research on nutrient dynamics in headwater wetlands is sparse. Our results indicated a significant positive correlation (Pearson coefficient ρ = 0.320) between soluble reactive phosphorus (SRP) in the headwater streams and the percentage of wetlands in these agriculture‐dominated catchments. This result suggests that headwater wetlands, and other wet riparian zones, are key sources of SRP in the headwater streams. Nitrate concentrations were positively correlated with % agricultural land cover (ρ = 0.316), consistent with previous studies, while SRP concentrations were negatively correlated with % agricultural land cover (ρ = −0.325). There was a significant positive correlation between SRP concentrations and discharge in some of the streams. Seasonal SRP trends appear to be closely related to temperature‐dependent seasonal changes in redox conditions, including levels of dissolved O2. Surficial geology had some influence on nitrate concentrations in the streams, which tended to be higher in catchments dominated by glacial till (till terrain), compared to catchments with extensive areas of outwash sand, in addition to till terrain.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".