Variation in particulate C : N : P stoichiometry across the Lake Erie watershed from tributaries to its outflow
Bibliographic record
Abstract
Abstract Human activities can cause large alterations in biogeochemical cycles of key nutrients such as carbon (C), nitrogen (N), and phosphorus (P). However, relatively little is known about how these changes alter the proportional fluxes of these elements across ecosystem boundaries from rivers to lakes. Here, we examined environmental factors influencing spatial and temporal variation in particulate C : N : P ratios across the Lake Erie watershed from its tributaries to its outflow. Throughout the study, particulate nutrient ratios ranged widely (C : N 2.0–25.8, C : P 32–530, N : P 3.7–122.9), but mean values were generally lower than previous estimates from different aquatic environments. Particulate C : N ratios varied the least across all environments, but C : P and N : P ratios increased between tributaries and coastal areas and throughout the growing season in coastal environments. These ratios also differed temporally in offshore waters as particulate C : P and N : P were higher in the spring and summer and lower in the fall and winter. Particulate C : P ratios also increased between the western/central and eastern basins indicating differential nutrient processing across the lake. These stoichiometric changes were associated with unique environmental factors among ecosystems as tributary stoichiometry was related to terrestrial land use and land cover, coastal ratios were a product of mixing between riverine and offshore waters, and offshore patterns were influenced by differences in temperature and particulate nutrient loading among basins. Overall, by studying changes in particulate C : N : P ratios across the Lake Erie watershed, our study demonstrates the power of using mass balance principles to study nutrient transformations along the aquatic continuum.
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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 | 0.000 | 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 | 0.001 | 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".