The role of wetland coverage within the near‐stream zone in predicting of seasonal stream export chemistry from forested headwater catchments
Bibliographic record
Abstract
Abstract Stream chemistry is often used to infer catchment‐scale biogeochemical processes. However, biogeochemical cycling in the near‐stream zone or hydrologically connected areas may exert a stronger influence on stream chemistry compared with cycling processes occurring in more distal parts of the catchment, particularly in dry seasons and in dry years. In this study, we tested the hypotheses that near‐stream wetland proportion is a better predictor of seasonal (winter, spring, summer, and fall) stream chemistry compared with whole‐catchment averages and that these relationships are stronger in dryer periods with lower hydrologic connectivity. We evaluated relationships between catchment wetland proportion and 16‐year average seasonal flow‐weighted concentrations of both biogeochemically active nutrients, dissolved organic carbon (DOC), nitrate (NO3‐N), total phosphorus (TP), as well as weathering products, calcium (Ca), magnesium (Mg), at ten headwater (<200 ha) forested catchments in south‐central Ontario, Canada. Wetland proportion across the entire catchment was the best predictor of DOC and TP in all seasons and years, whereas predictions of NO3‐N concentrations improved when only the proportion of wetland within the near‐stream zone was considered. This was particularly the case during dry years and dry seasons such as summer. In contrast, Ca and Mg showed no relationship with catchment wetland proportion at any scale or in any season. In forested headwater catchments, variable hydrologic connectivity of source areas to streams alters the role of the near‐stream zone environment, particularly during dry periods. The results also suggest that extent of riparian zone control may vary under changing patterns of hydrological connectivity. Predictions of biogeochemically active nutrients, particularly NO3‐N, can be improved by including near‐stream zone catchment morphology in landscape models.
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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 | 0.000 | 0.000 |
| 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".