Climate variability induced shifts in nitrogen loading from terrestrial to aquatic ecosystems
Bibliographic record
Abstract
Nitrogen is a critical nutrient linked to degradation of freshwater and marine ecosystems. The nitrogen inputs to terrestrial ecosys- tems and subsequent loadings to aquatic ecosystems have been doubled and changed the nitrogen cycle as population and hu- man activities increased over the past century. One of the consequences of human alter- nation of the nitrogen cycle is the eutrophication of marine and freshwater ecosystems. We tested if climate variability can change nitrogen loading from terrestrial to aquatic ecosystems. We used stream nitrogen concen- trations from 2,125 sites and climate data from 301 stations from 30 eco-regions across British Columbia, Canada, to test our ob- jective and to compare it with anthropogenic loading of nitrogen in the same regions. We show that elevated air temperature and associated precipitation resulted in increase in nitrogen loading from terrestrial to aquatic ecosystems. Furthermore, inorganic ni- trogen (IN) loading increased more rapidly than organic nitrogen (ON) with increasing air temperature. Each oC increment annu- al air temperature caused a 24% increase in nitrogen loading to aquatic ecosystems and a 22% increase in ratio of IN: ON concen- trations in stream water. We also show that the coastal mountains ecosystems seem to be more vulnerable to temperature induced nitrogen loss than the interior ecosystems. We suggest that cli- mate warming and elevated loading of nitrogen from terrestrial to aquatic ecosystems will have major implications for the quality of water in freshwater and coastal marine ecosystems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".