Climate Effects on Stream Nitrate Concentrations at 16 Forested Catchments in South Central Ontario
Bibliographic record
Abstract
Increased nitrate (NO3) concentrations in streamwaters draining forested catchments are reportedly an early indicator of nitrogen (N) saturation. Nitrate concentrations in streams draining 16 forested catchments in south central Ontario were monitored over a 16-year period, during which time N bulk deposition was relatively constant (approximately 9 kg ha(-1) yr(-1)). Mean annual NO3 concentrations in streams were both highly variable among catchments and among years, although patterns of annual concentration were similar among many catchments. Coherence analysis identified two stream groupings. Shallow soils, moderate slopes, low NO3 concentration, and a large wetland component characterized the first group. The second group had primarily upland characteristics including deeper soils, steeper slopes, higher NO3 concentrations, and a much smaller wetland component. Patterns in NO3 concentration in wetland-influenced streams appeared to be related to summer drought and cumulative frost depth, whereas NO3 concentrations in upland-draining streams appeared to be related to both mean annual air temperature and summer drought. Because a number of different climate parameters as well as the physical character of the catchments apparently influence NO3 export, NO3 concentrations in streams are not a good indicator of N saturation in this region.
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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 | 0.001 |
| 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".