The key role of nitrogen in boosting algal growth in Arctic rivers
Bibliographic record
Abstract
Increasing nutrient loads are impairing water quality and ecological status of aquatic ecosystems globally, but their effects on Arctic freshwaters remain less studied. Nutrients in Arctic freshwaters are increasing due to expanding anthropogenic land use and climate change induced alterations in nutrient leaching and transport. Also increased occurrences of non-native semelparous Pacific pink salmon ( Oncorhynchus gorbuscha) in the northern European rivers can enhance nutrient availability. These additional nutrients may be readily available for aquatic primary producers in often inherently (ultra-)oligotrophic Arctic freshwaters. We used an experimental field set-up with nutrient diffusing substrates to study temporal and spatial differences in the response of primary producers to increased phosphorus (P) and nitrogen (N) levels in Arctic rivers. Additional NO 3 -N enhanced algal growth (chlorophyll -a (Chl- a) amount) of epilithic biofilm. PO 4 -P alone had no effect, indicating N as the primary limiting factor for algal growth. This was particularly evident in autumn while early summer showed evidence for secondary P limitation. The response to nutrient additions was mostly dictated by the background availability of inorganic N. Our results suggest that Arctic rivers are highly sensitive to nutrient additions, especially N. Thus, any increase in nutrients can boost algal growth causing cascading effects throughout Arctic ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".