ISOTOPIC EVIDENCE FOR IN‐LAKE PRODUCTION OF ACCUMULATING NITRATE IN LAKE SUPERIOR
Bibliographic record
Abstract
A century-long increase in nitrate (NO3-) in the water column of Lake Superior is a classic example of recent nitrogen accumulation in ecosystems, but its cause and relationship to historical NO3- deposition is unknown. We used stable isotope ratios of oxygen and nitrogen in nitrate (delta18O-NO3 and delta15N-NO3) to examine its sources in this large lake, which represents 10% of the world's surficial liquid freshwater. The most parsimonious hypothesis to explain the rise in NO3- is that the lake is accruing NO3- deposited directly on the lake surface because it is too unproductive to completely assimilate all of it. Data for delta18O-NO3 in external sources and the water column, however, are inconsistent with this hypothesis. Instead, the isotopic evidence indicates strongly that the accumulating NO3- is almost entirely derived from nitrification occurring within the lake. While increases in atmospheric deposition of NO3- may have played a role in its buildup in the lake, other factors such as increases in NH4+ and dissolved organic nitrogen inputs from precipitation or rivers, increases in nitrogen fluxes from the sediments, and decreases in burial rates must also be considered as potential drivers of rising NO3-. The sustained accumulation of nitrogen in Lake Superior is thus more complex and incompletely understood than previously assumed.
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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 | 0.000 | 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".