Nitrogen and carbon uptake dynamics in Lake Superior
Bibliographic record
Abstract
Despite a fivefold rise in nitrate concentration over the last century, many fundamental aspects of Lake Superior's N and C cycles are still very poorly understood. We present here the first measurements of inorganic N uptake and in situ C uptake rates in Lake Superior, one of the largest lakes in the world. A profile of C uptake suggests that more than 95% of production occurs in the top 30 m with highest productivity to biomass ratio in the epilimnion. High C uptake:N uptake and particulate C: N ratio compared to the Redfield ratio (6.6) in the epilimnion suggests higher turnover rate of C compared to N in epilimnetic particles. Experiments performed over a range of typical environmental conditions suggest a strong temperature dependence of N uptake with maximum rates observed during the warmest stratified period. Lakewide N uptake estimates derived from a temperature‐based model suggest that on an annual basis, uptake is considerably higher than total N inputs from outside the lake. This difference indicates that the lake is recycling N rapidly, leading to a shorter turnover time in the water column than previously assumed. The long‐term buildup of nitrate in the lake has been hypothesized to arise from limited assimilation of nitrate entering the lake. In contrast, our results suggest that nitrate accumulating in the lake is a result of internal N cycling, a finding consistent with recent studies based on a nitrogen budget and NO 3 − stable isotope analyses.
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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 | 0.001 |
| 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".