An analysis of some components of the nitrogen cycle as affected by land use adjacent to the riparian zone of a southern Ontario stream
Bibliographic record
Abstract
To assess components of the N cycle such as ammonification, nitrification and net nitrogen mineralization, as well as extractable nitrate and ammonium (NH4+-N) pools, a study was conducted at Washington Creek, southwestern Ontario. Four different land use systems were studied: natural forest (NF), agricultural land + grass buffer (AP), livestock land (LS), and agriculture + rehabilitated buffer (RA). Soil net N-mineralization rates were calculated from ammonification and nitrification rates using the buried polyethylene bag method. Extractable nitrate and ammonium concentrations were obtained from samples taken as part of the sampling protocol for N-mineralization fluxes. Groundwater was also sampled from wells installed in the four land systems, and periphyton was sampled in stream waters associated with each land use system. Net soil N-mineralization rates were generally significantly higher (p<=0.05) in the RA (81 mg m-2 d-1) and AP (40 mg m -2 d-1) systems compared to the NF (24 mg m -2 d-1) and LS (18 mg m-2 d-1 ) systems, particularly in the fall. The same pattern was observed for extractable NO3--N over all seasons. However, for extractable NH4+-N, higher concentrations were found in the NF system in the fall of both 2003 and 2004; no differences were noted between land uses in other seasons. Chlorophyll 'a' and ash free dry mass (AFDM) of periphyton were significantly higher (p<=0.05) in the LS system. Chlorophyll 'a' and AFDM was poorly correlated with aqueous concentrations of nutrients, but was positively correlated with canopy openness. The groundwater flux ('Q') was highest in the NF and LS systems. In contrast, nitrate flux was highest in the AP system. This study showed that rates of N mineralization, nitrification and ammonification varied seasonally in each land use system. The RA and NF land use systems yielded different processing rates for the N species studied, when it was expected that these systems would have similar values after 25 years of rehabilitation. Future studies should focus on studying long-term N cycling recovery in rehabilitated riparian areas, such Washington Creek, and the relationship between buffer strip width, total buffered area, and stream basin hydrology.
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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 | 0.001 | 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".